ytzi/the-stack-dedup-python-filtered · Datasets at Hugging Face

1633a9fb3de8a2d02c1b973e0da5225da5fdee84

25,426

py

Python

create_coherency_dataset.py

UKPLab/acl20-dialogue-coherence-assessment

328b888855dc833b4b0c05c259ee7115f4219dbe

[ "MIT" ]

12

2020-05-03T12:41:53.000Z

2021-11-19T06:45:56.000Z

create_coherency_dataset.py

UKPLab/acl20-dialogue-coherence-assessment

328b888855dc833b4b0c05c259ee7115f4219dbe

[ "MIT" ]

2

2020-07-02T08:19:19.000Z

2021-12-03T16:58:02.000Z

create_coherency_dataset.py

UKPLab/acl20-dialogue-coherence-assessment

328b888855dc833b4b0c05c259ee7115f4219dbe

[ "MIT" ]

4

2020-08-27T08:36:55.000Z

2021-08-19T21:53:31.000Z

import math import os from copy import deepcopy from ast import literal_eval import pandas as pd from math import factorial import random from collections import Counter, defaultdict import sys from nltk import word_tokenize from tqdm import tqdm, trange import argparse import numpy as np import re import csv from sklearn.model_selection import train_test_split from swda.swda import CorpusReader, Transcript, Utterance act2word = {1:"inform",2:"question", 3:"directive", 4:"commissive"} def permute(sents, sent_DAs, amount): """ return a list of different! permuted sentences and their respective dialog acts """ """ if amount is greater than the possible amount of permutations, only the uniquely possible ones are returned """ assert len(sents) == len(sent_DAs), "length of permuted sentences and list of DAs must be equal" if amount == 0: return [] permutations = [list(range(len(sents)))] amount = min(amount, factorial(len(sents))-1) for i in range(amount): permutation = np.random.permutation(len(sents)) while permutation.tolist() in permutations: permutation = np.random.permutation(len(sents)) permutations.append(permutation.tolist()) return permutations[1:] #the first one is the original, which was included s.t. won't be generated def draw_rand_sent(act_utt_df, sent_len, amount): """ df is supposed to be a pandas dataframe with colums 'act' and 'utt' (utterance), with act being a number from 1 to 4 and utt being a sentence """ permutations = [] for _ in range(amount): (utt, da, name, ix) = draw_rand_sent_from_df(act_utt_df) sent_insert_ix = random.randint(0, sent_len-1) permutations.append((utt, da, name, ix, sent_insert_ix)) return permutations def main(): parser = argparse.ArgumentParser() parser.add_argument("--datadir", required=True, type=str, help="""The input directory where the files of the corpus are located. """) parser.add_argument("--corpus", required=True, type=str, help="""the name of the corpus to use, currently either 'DailyDialog' or 'Switchboard' """) parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--amount', type=int, default=20, help="random seed for initialization") parser.add_argument('--word2id', action='store_true', help= "convert the words to ids") parser.add_argument('--task', required=True, type=str, default="up", help="""for which task the dataset should be created. alternatives: up (utterance permutation) us (utterance sampling) hup (half utterance petrurbation) ui (utterance insertion, nothing directly added!)""") args = parser.parse_args() random.seed(args.seed) np.random.seed(args.seed) if args.word2id: f = open(os.path.join(args.datadir, "itos.txt"), "r") word2id_dict = dict() for i, word in enumerate(f): word2id_dict[word[:-1].lower()] = i word2id = lambda x: [word2id_dict[y] for y in x] # don't convert words to ids (yet). It gets done in the glove wrapper of mtl_coherence.py else: word2id = lambda x: x tokenizer = word_tokenize if args.corpus == 'DailyDialog': converter = DailyDialogConverter(args.datadir, tokenizer, word2id, task=args.task) converter.create_act_utt() elif args.corpus == 'Switchboard': converter = SwitchboardConverter(args.datadir, tokenizer, word2id, args.task, args.seed) converter.create_vocab() converter.convert_dset(amounts=args.amount) def getKeysByValue(dictOfElements, valueToFind): listOfKeys = list() for item in dictOfElements.items(): if item[1] == valueToFind: listOfKeys.append(item[0]) return listOfKeys def switchboard_da_mapping(): mapping_dict = dict({ "sd": 1, "b": 2, "sv": 3, "aa": 4, "%-": 5, "ba": 6, "qy": 7, "x": 8, "ny": 9, "fc": 10, "%": 11, "qw": 12, "nn": 13, "bk": 14, "h": 15, "qy^d": 16, "o": 17, "bh": 18, "^q": 19, "bf": 20, "na": 21, "ny^e": 22, "ad": 23, "^2": 24, "b^m": 25, "qo": 26, "qh": 27, "^h": 28, "ar": 29, "ng": 30, "nn^e": 31, "br": 32, "no": 33, "fp": 34, "qrr": 35, "arp": 36, "nd": 37, "t3": 38, "oo": 39, "co": 40, "cc": 41, "t1": 42, "bd": 43, "aap": 44, "am": 45, "^g": 46, "qw^d": 47, "fa": 48, "ft":49 }) d = defaultdict(lambda: 11) for (k, v) in mapping_dict.items(): d[k] = v return d if __name__ == "__main__": main()

39.977987

146

0.532801

163549f9139dc6999e9e0ca088584cc51b142caa

12,432

py

Python

tests/test_selections.py

swimmio/sqlalchemy_swimm

d24accb7792743cf586bd7062531d108e7063eba

[ "MIT" ]

null

tests/test_selections.py

swimmio/sqlalchemy_swimm

d24accb7792743cf586bd7062531d108e7063eba

[ "MIT" ]

null

tests/test_selections.py

swimmio/sqlalchemy_swimm

d24accb7792743cf586bd7062531d108e7063eba

[ "MIT" ]

null

import typing import pytest from src import selections

24.617822

91

0.178571

1635645909c86684dc1d01665725f73b3baa25cb

348

py

Python

tests/utils/test_clean_accounting_column.py

richardqiu/pyjanitor

aa3150e7b8e2adc4733ea206ea9c3093e21d4025

[ "MIT" ]

2

2020-09-06T22:11:01.000Z

2022-03-19T23:57:24.000Z

tests/utils/test_clean_accounting_column.py

richardqiu/pyjanitor

aa3150e7b8e2adc4733ea206ea9c3093e21d4025

[ "MIT" ]

1

2021-05-17T15:30:04.000Z

2021-07-29T09:39:56.000Z

tests/utils/test_clean_accounting_column.py

richardqiu/pyjanitor

aa3150e7b8e2adc4733ea206ea9c3093e21d4025

[ "MIT" ]

1

2020-08-10T20:30:20.000Z

import pytest from janitor.utils import _clean_accounting_column

21.75

61

0.761494

16369f4689956af64363c246df723fffbf5f3a5e

7,164

py

Python

downloadParagraph.py

icadot86/bert

42070209183dab3b5ff59b0dea1398a9538960f3

[ "Apache-2.0" ]

null

downloadParagraph.py

icadot86/bert

42070209183dab3b5ff59b0dea1398a9538960f3

[ "Apache-2.0" ]

null

downloadParagraph.py

icadot86/bert

42070209183dab3b5ff59b0dea1398a9538960f3

[ "Apache-2.0" ]

null

# coding=utf-8 import sys, getopt import urllib import requests import requests_cache import re import time from bs4 import BeautifulSoup from requests import Session sys.path.append("/home/taejoon1kim/BERT/my_bert") from utils.cacheUtils import cacheExist, writeCache, readCache, getDownloadCachePath from utils.path import BERT_INPUT_JSON, BERT_SEARCH_JSON WIKI_URL = "wikipedia.org" YOUTUBE_URL = "youtube.com/channel" NO_RESULT = "no_result" SEARCH_RESULT = { "WIKI" : {"title" : f"{NO_RESULT}", "link" : f"{NO_RESULT}"}, "FIRST" : {"title" : f"{NO_RESULT}", "link" : f"{NO_RESULT}"}, "YOUTUBE" : {"title" : f"{NO_RESULT}", "link" : f"{NO_RESULT}"}, "test_input.json" : f"{NO_RESULT}", "search_result.json" : f"{NO_RESULT}", "Q_TYPE" : f"{NO_RESULT}" } if __name__ == "__main__": main(sys.argv)

35.82

458

0.564768

1637357f64028a6c4c7d59c4294f21b8d56010e2

2,861

py

Python

data_io.py

LucasChenLC/courseManager2

3f91ea72dbc0a3f3afcc88c7f0959edb6c33adf9

[ "MIT" ]

null

data_io.py

LucasChenLC/courseManager2

3f91ea72dbc0a3f3afcc88c7f0959edb6c33adf9

[ "MIT" ]

null

data_io.py

LucasChenLC/courseManager2

3f91ea72dbc0a3f3afcc88c7f0959edb6c33adf9

[ "MIT" ]

null

from xml.dom.minidom import Document, parse ''' course_list = [] course_list.append(Course('Advance Math')) course_list.append(Course('Linear Algebra')) course_list.append(Course('Procedure Oriented Programming')) course_list.append(Course('Object Oriented Programming')) course_list[-1].add_pre_course(course_list, ['Procedure Oriented Programming']) course_list.append(Course('College Physics')) course_list[-1].add_pre_course(course_list, ['Advance Math']) course_list.append(Course('Digital Logic')) course_list[-1].add_pre_course(course_list, ['Procedure Oriented Programming']) course_list.append(Course('Computer Organization')) course_list[-1].add_pre_course(course_list, ['Advance Math', 'Procedure Oriented Programming', 'Digital Logic']) course_list.append(Course('Computer Architecture')) course_list[-1].add_pre_course(course_list, ['Advance Math', 'Procedure Oriented Programming', 'Digital Logic', 'Computer Organization']) save_data_xml(course_list, 'resource/data/data.xml') '''

37.644737

124

0.71828

163841fc5da39772ff971e9eff1ba89827ff6817

1,003

py

Python

tests/rules/test_git_rm_local_modifications.py

jlandrum/theheck

d2c008b6ca14220504be95f887253ddd9f5e9f72

[ "MIT" ]

null

tests/rules/test_git_rm_local_modifications.py

jlandrum/theheck

d2c008b6ca14220504be95f887253ddd9f5e9f72

[ "MIT" ]

null

tests/rules/test_git_rm_local_modifications.py

jlandrum/theheck

d2c008b6ca14220504be95f887253ddd9f5e9f72

[ "MIT" ]

null

import pytest from theheck.rules.git_rm_local_modifications import match, get_new_command from theheck.types import Command

34.586207

81

0.67996

16384fd421a05dbe791af899ad03aaf8e20b6076

6,078

py

Python

application.py

statisticsnorway/microdata-data-service

d477b7b75589d4c977771122558c948c040a1106

[ "Apache-2.0" ]

null

application.py

statisticsnorway/microdata-data-service

d477b7b75589d4c977771122558c948c040a1106

[ "Apache-2.0" ]

7

2021-10-08T13:40:33.000Z

2022-02-04T10:37:55.000Z

application.py

statisticsnorway/microdata-data-service

d477b7b75589d4c977771122558c948c040a1106

[ "Apache-2.0" ]

null

import logging import json_logging import tomlkit import uvicorn from fastapi import FastAPI, status from fastapi.encoders import jsonable_encoder from fastapi.openapi.docs import ( get_redoc_html, get_swagger_ui_html, get_swagger_ui_oauth2_redirect_html, ) from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles from starlette.responses import PlainTextResponse, Response from data_service.api.data_api import data_router from data_service.api.observability_api import observability_router from data_service.config import config from data_service.core.processor import NotFoundException from data_service.core.filters import EmptyResultSetException """ Self-hosting JavaScript and CSS for docs https://fastapi.tiangolo.com/advanced/extending-openapi/#self-hosting-javascript-and-css-for-docs """ data_service_app = FastAPI(docs_url=None, redoc_url=None) data_service_app.mount("/static", StaticFiles(directory="static"), name="static") data_service_app.include_router(data_router) data_service_app.include_router(observability_router) def _get_project_meta(): with open('./pyproject.toml') as pyproject: file_contents = pyproject.read() return tomlkit.parse(file_contents)['tool']['poetry'] pkg_meta = _get_project_meta() if __name__ == "__main__": uvicorn.run(data_service_app, host="0.0.0.0", port=8000)

33.766667

109

0.74054

16386e8f49ac83e2f9c436adbc056266858401ad

18,764

py

Python

graspologic/embed/n2v.py

dtborders/graspologic

8ea9a47cabe35ad28ec9d381e525358c2027f619

[ "MIT" ]

null

graspologic/embed/n2v.py

dtborders/graspologic

8ea9a47cabe35ad28ec9d381e525358c2027f619

[ "MIT" ]

null

graspologic/embed/n2v.py

dtborders/graspologic

8ea9a47cabe35ad28ec9d381e525358c2027f619

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation and contributors. # Licensed under the MIT License. import logging import math import time from typing import Any, List, Optional, Tuple, Union import networkx as nx import numpy as np from ..utils import remap_node_ids def node2vec_embed( graph: Union[nx.Graph, nx.DiGraph], num_walks: int = 10, walk_length: int = 80, return_hyperparameter: float = 1.0, inout_hyperparameter: float = 1.0, dimensions: int = 128, window_size: int = 10, workers: int = 8, iterations: int = 1, interpolate_walk_lengths_by_node_degree: bool = True, random_seed: Optional[int] = None, ) -> Tuple[np.array, List[Any]]: """ Generates a node2vec embedding from a given graph. Will follow the word2vec algorithm to create the embedding. Parameters ---------- graph: Union[nx.Graph, nx.DiGraph] A networkx graph or digraph. A multigraph should be turned into a non-multigraph so that the calling user properly handles the multi-edges (i.e. aggregate weights or take last edge weight). If the graph is unweighted, the weight of each edge will default to 1. num_walks : int Number of walks per source. Default is 10. walk_length: int Length of walk per source. Default is 80. return_hyperparameter : float Return hyperparameter (p). Default is 1.0 inout_hyperparameter : float Inout hyperparameter (q). Default is 1.0 dimensions : int Dimensionality of the word vectors. Default is 128. window_size : int Maximum distance between the current and predicted word within a sentence. Default is 10. workers : int Use these many worker threads to train the model. Default is 8. iterations : int Number of epochs in stochastic gradient descent (SGD) interpolate_walk_lengths_by_node_degree : bool Use a dynamic walk length that corresponds to each nodes degree. If the node is in the bottom 20 percentile, default to a walk length of 1. If it is in the top 10 percentile, use ``walk_length``. If it is in the 20-80 percentiles, linearly interpolate between 1 and ``walk_length``. This will reduce lower degree nodes from biasing your resulting embedding. If a low degree node has the same number of walks as a high degree node (which it will if this setting is not on), then the lower degree nodes will take a smaller breadth of random walks when compared to the high degree nodes. This will result in your lower degree walks dominating your higher degree nodes. random_seed : int Seed to be used for reproducible results. Default is None and will produce a random output. Note that for a fully deterministically-reproducible run, you must also limit to a single worker thread (`workers=1`), to eliminate ordering jitter from OS thread scheduling. In addition the environment variable ``PYTHONHASHSEED`` must be set to control hash randomization. Returns ------- Tuple[np.array, List[Any]] A tuple containing a matrix, with each row index corresponding to the embedding for each node. The tuple also contains a vector containing the corresponding vertex labels for each row in the matrix. The matrix and vector are positionally correlated. Notes ----- The original reference implementation of node2vec comes from Aditya Grover from https://github.com/aditya-grover/node2vec/. Further details on the Alias Method used in this functionality can be found at https://lips.cs.princeton.edu/the-alias-method-efficient-sampling-with-many-discrete-outcomes/ References ---------- .. [1] Aditya Grover and Jure Leskovec "node2vec: Scalable Feature Learning for Networks." Knowledge Discovery and Data Mining, 2016. """ _preconditions( graph, num_walks, walk_length, return_hyperparameter, inout_hyperparameter, dimensions, window_size, workers, iterations, interpolate_walk_lengths_by_node_degree, ) random_state = np.random.RandomState(seed=random_seed) node2vec_graph = _Node2VecGraph( graph, return_hyperparameter, inout_hyperparameter, random_state ) logging.info( f"Starting preprocessing of transition probabilities on graph with {str(len(graph.nodes()))} nodes and " f"{str(len(graph.edges()))} edges" ) start = time.time() logging.info(f"Starting at time {str(start)}") node2vec_graph._preprocess_transition_probabilities() logging.info(f"Simulating walks on graph at time {str(time.time())}") walks = node2vec_graph._simulate_walks( num_walks, walk_length, interpolate_walk_lengths_by_node_degree ) logging.info(f"Learning embeddings at time {str(time.time())}") model = _learn_embeddings( walks, dimensions, window_size, workers, iterations, random_seed ) end = time.time() logging.info( f"Completed. Ending time is {str(end)} Elapsed time is {str(start - end)}" ) labels = node2vec_graph.original_graph.nodes() remapped_labels = node2vec_graph.label_map_to_string return ( np.array([model.wv.get_vector(remapped_labels[node]) for node in labels]), labels, ) def _learn_embeddings( walks: List[Any], dimensions: int, window_size: int, workers: int, iterations: int, random_seed: Optional[int], ): """ Learn embeddings by optimizing the skip-gram objective using SGD. """ from gensim.models import Word2Vec walks = [list(map(str, walk)) for walk in walks] # Documentation - https://radimrehurek.com/gensim/models/word2vec.html model = Word2Vec( walks, size=dimensions, window=window_size, min_count=0, sg=1, # Training algorithm: 1 for skip-gram; otherwise CBOW workers=workers, iter=iterations, seed=random_seed, ) return model def _alias_setup(probabilities: List[float]): """ Compute utility lists for non-uniform sampling from discrete distributions. Refer to https://lips.cs.princeton.edu/the-alias-method-efficient-sampling-with-many-discrete-outcomes/ for details """ number_of_outcomes = len(probabilities) alias = np.zeros(number_of_outcomes) sampled_probabilities = np.zeros(number_of_outcomes, dtype=int) smaller = [] larger = [] for i, prob in enumerate(probabilities): alias[i] = number_of_outcomes * prob if alias[i] < 1.0: smaller.append(i) else: larger.append(i) while len(smaller) > 0 and len(larger) > 0: small = smaller.pop() large = larger.pop() sampled_probabilities[small] = large alias[large] = alias[large] + alias[small] - 1.0 if alias[large] < 1.0: smaller.append(large) else: larger.append(large) return sampled_probabilities, alias def _alias_draw( probabilities: List[float], alias: List[float], random_state: np.random.RandomState ): """ Draw sample from a non-uniform discrete distribution using alias sampling. """ number_of_outcomes = len(probabilities) random_index = int(np.floor(random_state.rand() * number_of_outcomes)) if random_state.rand() < alias[random_index]: return random_index else: return probabilities[random_index]

35.537879

127

0.627052

1638d587cabcf4138e331d614308389b13e85fb7

8,421

py

Python

bot.py

NotBlizzard/blizzybot

41a6f07e4d3bb97772b07aa9d6a3af935b78fb9a

[ "MIT" ]

null

bot.py

NotBlizzard/blizzybot

41a6f07e4d3bb97772b07aa9d6a3af935b78fb9a

[ "MIT" ]

null

bot.py

NotBlizzard/blizzybot

41a6f07e4d3bb97772b07aa9d6a3af935b78fb9a

[ "MIT" ]

null

# bot.py # TODO: # organize imports # organize from websocket import create_connection from threading import Thread from battle import Battle import commands import traceback import requests import inspect import json from fractions import Fraction import random import time import sys import re import os from learn import Learn

36.141631

131

0.517278

16391df203c1efac2e1f8b82d3e69209d5e07f18

10,758

py

Python

stRT/tdr/widgets/changes.py

Yao-14/stAnalysis

d08483ce581f5b03cfcad8be500aaa64b0293f74

[ "BSD-3-Clause" ]

null

stRT/tdr/widgets/changes.py

Yao-14/stAnalysis

d08483ce581f5b03cfcad8be500aaa64b0293f74

[ "BSD-3-Clause" ]

null

stRT/tdr/widgets/changes.py

Yao-14/stAnalysis

d08483ce581f5b03cfcad8be500aaa64b0293f74

[ "BSD-3-Clause" ]

null

from typing import Optional, Tuple, Union import numpy as np import pandas as pd import pyvista as pv from pyvista import DataSet, MultiBlock, PolyData, UnstructuredGrid try: from typing import Literal except ImportError: from typing_extensions import Literal from .ddrtree import DDRTree, cal_ncenter from .slice import euclidean_distance, three_d_slice #################################### # Changes along a vector direction # #################################### ################################# # Changes along the model shape # ################################# ############################## # Changes along the branches # ############################## def ElPiGraph_tree( X: np.ndarray, NumNodes: int = 50, **kwargs, ) -> Tuple[np.ndarray, np.ndarray]: """ Generate a principal elastic tree. Reference: Albergante et al. (2020), Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph. Args: X: DxN, data matrix list. NumNodes: The number of nodes of the principal graph. Use a range of 10 to 100 for ElPiGraph approach. **kwargs: Other parameters used in elpigraph.computeElasticPrincipalTree. For details, please see: https://github.com/j-bac/elpigraph-python/blob/master/elpigraph/_topologies.py Returns: nodes: The nodes in the principal tree. edges: The edges between nodes in the principal tree. """ try: import elpigraph except ImportError: raise ImportError( "You need to install the package `elpigraph-python`." "\nInstall elpigraph-python via `pip install git+https://github.com/j-bac/elpigraph-python.git`." ) ElPiGraph_kwargs = { "alpha": 0.01, "FinalEnergy": "Penalized", "StoreGraphEvolution": True, "GPU": False, } ElPiGraph_kwargs.update(kwargs) if ElPiGraph_kwargs["GPU"] is True: try: import cupy except ImportError: raise ImportError( "You need to install the package `cupy`." "\nInstall cupy via `pip install cupy-cuda113`." ) elpi_tree = elpigraph.computeElasticPrincipalTree( X=np.asarray(X), NumNodes=NumNodes, **ElPiGraph_kwargs ) nodes = elpi_tree[0]["NodePositions"] # ['AllNodePositions'][k] matrix_edges_weights = elpi_tree[0]["ElasticMatrix"] # ['AllElasticMatrices'][k] matrix_edges_weights = np.triu(matrix_edges_weights, 1) edges = np.array(np.nonzero(matrix_edges_weights), dtype=int).transpose() return nodes, edges def SimplePPT_tree( X: np.ndarray, NumNodes: int = 50, **kwargs, ) -> Tuple[np.ndarray, np.ndarray]: """ Generate a simple principal tree. Reference: Mao et al. (2015), SimplePPT: A simple principal tree algorithm, SIAM International Conference on Data Mining. Args: X: DxN, data matrix list. NumNodes: The number of nodes of the principal graph. Use a range of 100 to 2000 for PPT approach. **kwargs: Other parameters used in simpleppt.ppt. For details, please see: https://github.com/LouisFaure/simpleppt/blob/main/simpleppt/ppt.py Returns: nodes: The nodes in the principal tree. edges: The edges between nodes in the principal tree. """ try: import igraph import simpleppt except ImportError: raise ImportError( "You need to install the package `simpleppt` and `igraph`." "\nInstall simpleppt via `pip install -U simpleppt`." "\nInstall igraph via `pip install -U igraph`" ) SimplePPT_kwargs = { "seed": 1, "lam": 10, } SimplePPT_kwargs.update(kwargs) X = np.asarray(X) ppt_tree = simpleppt.ppt(X=X, Nodes=NumNodes, **SimplePPT_kwargs) R = ppt_tree.R nodes = (np.dot(X.T, R) / R.sum(axis=0)).T B = ppt_tree.B edges = np.array( igraph.Graph.Adjacency((B > 0).tolist(), mode="undirected").get_edgelist() ) return nodes, edges def map_points_to_branch( model: Union[PolyData, UnstructuredGrid], nodes: np.ndarray, spatial_key: Optional[str] = None, key_added: Optional[str] = "nodes", inplace: bool = False, **kwargs, ): """ Find the closest principal tree node to any point in the model through KDTree. Args: model: A reconstruct model. nodes: The nodes in the principal tree. spatial_key: The key that corresponds to the coordinates of the point in the model. If spatial_key is None, the coordinates are model.points. key_added: The key under which to add the nodes labels. inplace: Updates model in-place. kwargs: Other parameters used in scipy.spatial.KDTree. Returns: A model, which contains the following properties: `model.point_data[key_added]`, the nodes labels array. """ from scipy.spatial import KDTree model = model.copy() if not inplace else model X = model.points if spatial_key is None else model[spatial_key] nodes_kdtree = KDTree(np.asarray(nodes), **kwargs) _, ii = nodes_kdtree.query(np.asarray(X), k=1) model.point_data[key_added] = ii return model if not inplace else None def map_gene_to_branch( model: Union[PolyData, UnstructuredGrid], tree: PolyData, key: Union[str, list], nodes_key: Optional[str] = "nodes", inplace: bool = False, ): """ Find the closest principal tree node to any point in the model through KDTree. Args: model: A reconstruct model contains the gene expression label. tree: A three-dims principal tree model contains the nodes label. key: The key that corresponds to the gene expression. nodes_key: The key that corresponds to the coordinates of the nodes in the tree. inplace: Updates tree model in-place. Returns: A tree, which contains the following properties: `tree.point_data[key]`, the gene expression array. """ model = model.copy() model_data = pd.DataFrame(model[nodes_key], columns=["nodes_id"]) key = [key] if isinstance(key, str) else key for sub_key in key: model_data[sub_key] = np.asarray(model[sub_key]) model_data = model_data.groupby(by="nodes_id").sum() model_data["nodes_id"] = model_data.index model_data.index = range(len(model_data.index)) tree = tree.copy() if not inplace else tree tree_data = pd.DataFrame(tree[nodes_key], columns=["nodes_id"]) tree_data = pd.merge(tree_data, model_data, how="outer", on="nodes_id") tree_data.fillna(value=0, inplace=True) for sub_key in key: tree.point_data[sub_key] = tree_data[sub_key].values return tree if not inplace else None def construct_tree_model( nodes: np.ndarray, edges: np.ndarray, key_added: Optional[str] = "nodes", ) -> PolyData: """ Construct a principal tree model. Args: nodes: The nodes in the principal tree. edges: The edges between nodes in the principal tree. key_added: The key under which to add the nodes labels. Returns: A three-dims principal tree model, which contains the following properties: `tree_model.point_data[key_added]`, the nodes labels array. """ padding = np.empty(edges.shape[0], int) * 2 padding[:] = 2 edges_w_padding = np.vstack((padding, edges.T)).T tree_model = pv.PolyData(nodes, edges_w_padding) tree_model.point_data[key_added] = np.arange(0, len(nodes), 1) return tree_model

31.734513

125

0.635899

16394617ff3197501b57f08cd314d25d52093a16

842

py

Python

test/test_add_group.py

nkoshkina/Python_Training3

e917440d37883dbcaa527a0700bcfa1478a1c1ce

[ "Apache-2.0" ]

null

test/test_add_group.py

nkoshkina/Python_Training3

e917440d37883dbcaa527a0700bcfa1478a1c1ce

[ "Apache-2.0" ]

null

test/test_add_group.py

nkoshkina/Python_Training3

e917440d37883dbcaa527a0700bcfa1478a1c1ce

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from model.group import Group import pytest import allure_pytest

36.608696

93

0.693587

163995382115c67384ddb8a508342f8bf7650216

1,164

py

Python

cyberbrain/frame_tree.py

testinggg-art/Cyberbrain

e38c74c174e23aa386d005b03f09b30aa1b3a0ae

[ "MIT" ]

null

cyberbrain/frame_tree.py

testinggg-art/Cyberbrain

e38c74c174e23aa386d005b03f09b30aa1b3a0ae

[ "MIT" ]

null

cyberbrain/frame_tree.py

testinggg-art/Cyberbrain

e38c74c174e23aa386d005b03f09b30aa1b3a0ae

[ "MIT" ]

null

from __future__ import annotations from .frame import Frame from .generated.communication_pb2 import CursorPosition

28.390244

87

0.670103

163c66ec8f6a6a9ebf21f694414728829c5d030d

7,851

py

Python

src/otp_yubikey/models.py

moggers87/django-otp-yubikey

2d7cf9dc91ba57b65aa62254532997cc1e6261dd

[ "BSD-2-Clause" ]

null

src/otp_yubikey/models.py

moggers87/django-otp-yubikey

2d7cf9dc91ba57b65aa62254532997cc1e6261dd

[ "BSD-2-Clause" ]

null

src/otp_yubikey/models.py

moggers87/django-otp-yubikey

2d7cf9dc91ba57b65aa62254532997cc1e6261dd

[ "BSD-2-Clause" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals from base64 import b64decode from binascii import hexlify, unhexlify from struct import pack import six from django.db import models from django.utils.encoding import force_text from django_otp.models import Device from django_otp.util import hex_validator, random_hex from yubiotp.client import YubiClient10, YubiClient11, YubiClient20 from yubiotp.modhex import modhex from yubiotp.otp import decode_otp

27.644366

139

0.640683

163cbfb7a11f70465bec9d58e23cdc35d6fe4e2c

5,976

py

Python

v1/hsvfilter.py

gavinIRL/RHBot

1e22ae5ca7b67ebd6a72c23d9f46d5a8eb6e99cf

[ "MIT" ]

null

v1/hsvfilter.py

gavinIRL/RHBot

1e22ae5ca7b67ebd6a72c23d9f46d5a8eb6e99cf

[ "MIT" ]

60

2021-03-29T14:29:49.000Z

2021-05-03T06:06:19.000Z

v1/hsvfilter.py

gavinIRL/RHBot

1e22ae5ca7b67ebd6a72c23d9f46d5a8eb6e99cf

[ "MIT" ]

null

import typing # custom data structure to hold the state of an HSV filter # Putting this here out of the way as it's a chonk # For a given item string case it will return the optimal filter and the correct position to look def grab_object_preset(object_name=None, **kwargs) -> typing.Tuple[HsvFilter, list]: if object_name is None: #print("Using default filter") return HsvFilter(0, 0, 0, 255, 255, 255, 0, 0, 0, 0), [3, 32, 1280, 794] if object_name == "dungeon_check": return HsvFilter(0, 73, 94, 106, 255, 255, 0, 0, 0, 0), [1083, 295, 1188, 368] if object_name == "enemy_map_loc": #print("Using enemy location filter") if kwargs.get("big_map"): return HsvFilter(0, 128, 82, 8, 255, 255, 0, 66, 30, 34), [485, 280, 900, 734] return HsvFilter(0, 128, 82, 8, 255, 255, 0, 66, 30, 34), [1100, 50, 1260, 210] if object_name == "player_map_loc": if kwargs.get("big_map"): return HsvFilter(31, 94, 86, 73, 255, 255, 0, 0, 0, 0), [485, 280, 900, 734] return HsvFilter(31, 94, 86, 73, 255, 255, 0, 0, 0, 0), [1100, 50, 1260, 210] if object_name == "other_player_map_loc": if kwargs.get("big_map"): return HsvFilter(16, 172, 194, 32, 255, 255, 0, 0, 70, 37), [485, 280, 900, 734] return HsvFilter(16, 172, 194, 32, 255, 255, 0, 0, 70, 37), [1100, 50, 1260, 210] if object_name == "loot_distant": return HsvFilter(14, 116, 33, 32, 210, 59, 16, 0, 3, 0), [10, 145, 1084, 684] if object_name == "loot_near": return HsvFilter(0, 155, 135, 31, 240, 217, 0, 0, 0, 0), [460, 420, 855, 710] if object_name == "prompt_press_x_pickup": return HsvFilter(78, 110, 110, 97, 189, 255, 0, 0, 0, 0), [1080, 660, 1255, 725] if object_name == "message_section_cleared": return HsvFilter(0, 0, 214, 179, 65, 255, 0, 0, 0, 17), [464, 600, 855, 680] if object_name == "message_go": return HsvFilter(32, 114, 89, 58, 255, 255, 0, 12, 0, 0), [600, 222, 700, 275] if object_name == "enemy_nametag": return HsvFilter(49, 0, 139, 91, 30, 197, 0, 0, 40, 38), [10, 145, 1084, 684] if object_name == "message_boss_encounter": return HsvFilter(0, 92, 128, 13, 255, 255, 0, 0, 0, 0), [630, 520, 1120, 680] if object_name == "display_boss_name_and_healthbar": return HsvFilter(0, 92, 123, 29, 255, 255, 0, 0, 0, 20), [415, 533, 888, 700] if object_name == "loot_chest_normal": # This is a difficult one to separate return HsvFilter(0, 34, 38, 28, 152, 124, 0, 0, 5, 12), [10, 145, 1084, 684] if object_name == "map_outline": if kwargs.get("big_map"): return HsvFilter(0, 128, 82, 8, 255, 255, 0, 66, 30, 34), [485, 280, 900, 734] return HsvFilter(0, 128, 82, 8, 255, 255, 0, 66, 30, 34), [1100, 50, 1260, 210] if object_name == "gate_map_pos": # This is a very difficult one to separate if kwargs.get("big_map"): return HsvFilter(0, 128, 82, 8, 255, 255, 0, 66, 30, 34), [485, 280, 900, 734] return HsvFilter(0, 128, 82, 8, 255, 255, 0, 66, 30, 34), [1100, 50, 1260, 210] if object_name == "prompt_move_reward_screen": return HsvFilter(72, 98, 92, 105, 255, 225, 0, 54, 24, 38) if object_name == "prompt_select_card": return HsvFilter(79, 149, 140, 255, 255, 255, 0, 0, 0, 0) if object_name == "event_chest_special_appear": return HsvFilter(0, 124, 62, 88, 217, 246, 0, 0, 0, 0) if object_name == "inventory_green_item": return HsvFilter(37, 147, 0, 61, 255, 255, 0, 0, 0, 0) if object_name == "inventory_blue_item": return HsvFilter(79, 169, 0, 109, 246, 188, 0, 0, 0, 0) if object_name == "inventory_yellow_item": # This is a dangerous one as it can barely # distinguish against green items and vice versa return HsvFilter(19, 91, 107, 31, 168, 181, 0, 11, 32, 21) if object_name == "inventory_purple_item": return HsvFilter(126, 153, 0, 255, 255, 255, 0, 0, 0, 0) if object_name == "button_repair": return None, [208, 600] # These are all To be done later if object_name == "event_card_trade": return HsvFilter(0, 0, 0, 255, 255, 255, 0, 0, 0, 0) if object_name == "event_otherworld": return HsvFilter(0, 0, 0, 255, 255, 255, 0, 0, 0, 0) if object_name == "loot_chest_special": if kwargs.get("big_map"): return HsvFilter(0, 0, 0, 255, 255, 255, 0, 0, 0, 0), [10, 145, 1084, 684] return HsvFilter(0, 0, 0, 255, 255, 255, 0, 0, 0, 0), [10, 145, 1084, 684] if object_name == "cards": return HsvFilter(0, 0, 0, 255, 255, 255, 0, 0, 0, 0), [735, 32, 1085, 100] if object_name == "enemy_arrow": return HsvFilter(0, 0, 0, 255, 255, 255, 0, 0, 0, 0), [10, 145, 1084, 684] # Buttons for clicking, known positions if object_name == "button_explore_again": return None, [] if object_name == "button_choose_map": return None, [] if object_name == "button_open_store": return None, [] if object_name == "button_go_town": return None, [] if object_name == "button_inv_equipment": return None, [] if object_name == "button_inv_consume": return None, [] if object_name == "button_inv_other": return None, [] if object_name == "button_repair_confirm": return None, [] if object_name == "inv_grid_location": return None, [533+44*kwargs.get("col"), 277+44*kwargs.get("row")]

49.38843

97

0.593373

163d64f557e7427d0b9ba345ed63cc3b52a618e5

14,278

py

Python

glue/core/tests/test_state_objects.py

HPLegion/glue

1843787ccb4de852dfe103ff58473da13faccf5f

[ "BSD-3-Clause" ]

null

glue/core/tests/test_state_objects.py

HPLegion/glue

1843787ccb4de852dfe103ff58473da13faccf5f

[ "BSD-3-Clause" ]

null

glue/core/tests/test_state_objects.py

HPLegion/glue

1843787ccb4de852dfe103ff58473da13faccf5f

[ "BSD-3-Clause" ]

null

import numpy as np from numpy.testing import assert_allclose from echo import CallbackProperty, ListCallbackProperty from glue.core import Data, DataCollection from .test_state import clone from ..state_objects import (State, StateAttributeLimitsHelper, StateAttributeSingleValueHelper, StateAttributeHistogramHelper) EXPECTED_STR = """ a: 2 b: hello flat: <CallbackList with 3 elements> nested: <CallbackList with 3 elements> """ EXPECTED_REPR = """ <SimpleTestState a: 2 b: hello flat: <CallbackList with 3 elements> nested: <CallbackList with 3 elements> > """ def test_histogram_helper_common_n_bin(): data = Data(x=[-3.2, 4.3, 2.2], y=['a', 'f', 'd'], z=[1.1, 2.3, 1.2], label='test_data') state = SimpleState() helper = StateAttributeHistogramHelper(state, attribute='comp', lower='x_min', upper='x_max', n_bin='n_bin', common_n_bin='common') state.data = data state.comp = data.id['x'] state.n_bin = 9 state.comp = data.id['y'] assert state.n_bin == 3 state.comp = data.id['z'] assert state.n_bin == 15 state.n_bin = 12 state.common = True state.comp = data.id['x'] assert state.n_bin == 12 state.n_bin = 11 state.comp = data.id['y'] assert state.n_bin == 3 state.comp = data.id['z'] assert state.n_bin == 11 state.common = False state.n_bin = 13 state.comp = data.id['x'] assert state.n_bin == 11 def test_histogram_helper_common_n_bin_active(): # Make sure that common_n_bin works as expected if True from start data = Data(x=[-3.2, 4.3, 2.2], y=['a', 'f', 'd'], z=[1.1, 2.3, 1.2], label='test_data') state = SimpleState() helper = StateAttributeHistogramHelper(state, attribute='comp', lower='x_min', upper='x_max', n_bin='n_bin', common_n_bin='common') state.data = data state.comp = data.id['x'] state.n_bin = 9 state.comp = data.id['z'] assert state.n_bin == 9 state.n_bin = 12 state.common = True state.comp = data.id['x'] assert state.n_bin == 12 state.n_bin = 11 state.comp = data.id['y'] assert state.n_bin == 3 state.comp = data.id['z'] assert state.n_bin == 11 state.common = False state.n_bin = 13 state.comp = data.id['x'] assert state.n_bin == 11 def test_limits_helper_initial_values(): # Regression test for a bug that occurred if the limits cache was empty # but some attributes were set to values - in this case we don't want to # override the existing values. data = Data(x=np.linspace(-100, 100, 10000), y=np.linspace(2, 3, 10000), label='test_data') state = SimpleState() state.lower = 1 state.upper = 2 state.comp = data.id['x'] helper = StateAttributeLimitsHelper(state, attribute='comp', lower='lower', upper='upper') assert helper.lower == 1 assert helper.upper == 2

27.832359

96

0.588178

163d903313e3ca0e241b2c27dfd7fddcb15bbfdb

287

py

Python

ecommerce_api/core/cart/exceptions.py

victormartinez/ecommerceapi

a887d9e938050c15ebf52001f63d7aa7f33fa5ee

[ "MIT" ]

null

ecommerce_api/core/cart/exceptions.py

victormartinez/ecommerceapi

a887d9e938050c15ebf52001f63d7aa7f33fa5ee

[ "MIT" ]

null

ecommerce_api/core/cart/exceptions.py

victormartinez/ecommerceapi

a887d9e938050c15ebf52001f63d7aa7f33fa5ee

[ "MIT" ]

null

from typing import Iterable, Optional

31.888889

68

0.700348

163dc7048c89ab3ce7a0707b33435bed5fbe6660

6,742

py

Python

test/unit/test_record.py

jsoref/neo4j-python-driver

32c130c9a975dbf8c0d345b362d096b5e1dd3e5b

[ "Apache-2.0" ]

null

test/unit/test_record.py

jsoref/neo4j-python-driver

32c130c9a975dbf8c0d345b362d096b5e1dd3e5b

[ "Apache-2.0" ]

null

test/unit/test_record.py

jsoref/neo4j-python-driver

32c130c9a975dbf8c0d345b362d096b5e1dd3e5b

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- encoding: utf-8 -*- # Copyright (c) 2002-2018 "Neo Technology," # Network Engine for Objects in Lund AB [http://neotechnology.com] # # This file is part of Neo4j. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from unittest import TestCase from neo4j.v1 import Record

43.496774

116

0.590923

163ee50e70aae9c38787e48d9c60c83c946fac91

9,923

py

Python

tests/integration_tests/test_dashboards.py

hugocool/explainerdashboard

e725528c3d94a1a45b51bd9632686d0697274f54

[ "MIT" ]

1

2021-11-19T09:30:56.000Z

tests/integration_tests/test_dashboards.py

hugocool/explainerdashboard

e725528c3d94a1a45b51bd9632686d0697274f54

[ "MIT" ]

null

tests/integration_tests/test_dashboards.py

hugocool/explainerdashboard

e725528c3d94a1a45b51bd9632686d0697274f54

[ "MIT" ]

null

import dash from catboost import CatBoostClassifier, CatBoostRegressor from xgboost import XGBClassifier, XGBRegressor from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from explainerdashboard.explainers import ClassifierExplainer, RegressionExplainer from explainerdashboard.datasets import titanic_survive, titanic_fare, titanic_embarked, titanic_names from explainerdashboard.dashboards import ExplainerDashboard

44.698198

102

0.665121

163f5e0eb3de89d92ad7d61128630ed72fcd3690

1,079

py

Python

code/scripts/GeneratePNG_Preview_AsIs.py

dgrechka/bengaliai-cv19

9ef15c5b140628337ae6efe0d76e7ec5d291dc17

[ "MIT" ]

null

code/scripts/GeneratePNG_Preview_AsIs.py

dgrechka/bengaliai-cv19

9ef15c5b140628337ae6efe0d76e7ec5d291dc17

[ "MIT" ]

null

code/scripts/GeneratePNG_Preview_AsIs.py

dgrechka/bengaliai-cv19

9ef15c5b140628337ae6efe0d76e7ec5d291dc17

[ "MIT" ]

null

import tensorflow as tf import sys import os from glob import glob import png sys.path.append(os.path.join(__file__,'..','..')) from tfDataIngest import tfDataSetParquet as tfDsParquet inputDataDir = sys.argv[1] outputDir = sys.argv[2] # test app if __name__ == "__main__": files = glob(os.path.join(inputDataDir,"train*.parquet")) print("Found {0} parquet files in input dir {1}".format(len(files),inputDataDir)) print("First is {0}".format(files[0])) ds = tfDsParquet.create_parquet_dataset([files[0]]) for element in ds.as_numpy_iterator(): #print("Iterating...") sampleId,pixels = element sampleId = sampleId.decode("utf-8") fileName = os.path.join(outputDir,"{0}.png".format(sampleId)) png.from_array(pixels, mode="L").save(fileName) #print(element) #print("sample name is {0}".format(sampleId)) #print(sampleIds.shape) #print(pixels.shape) # a += 1 # if a > 10: # break print("Done") #print("{0} elements in the dataset".format(len(ds.)))

29.972222

85

0.636701

1640d2033b3fc61dda0183c87b5baa9f8cbed3bd

2,763

py

Python

widgets/datepicker_ctrl/codegen.py

RSabet/wxGlade

8b62eb8397308e60977857455b2765727b1b940f

[ "MIT" ]

225

2018-03-26T11:23:22.000Z

2022-03-24T09:44:08.000Z

widgets/datepicker_ctrl/codegen.py

RSabet/wxGlade

8b62eb8397308e60977857455b2765727b1b940f

[ "MIT" ]

403

2018-01-03T19:47:28.000Z

2018-03-23T17:43:39.000Z

widgets/datepicker_ctrl/codegen.py

DietmarSchwertberger/wxGlade

8e78cdc509d458cc896d47315e19f3daa6c09213

[ "MIT" ]

47

2018-04-08T16:48:38.000Z

2021-12-21T20:08:44.000Z

"""\ Code generator functions for wxDatePickerCtrl objects @copyright: 2002-2007 Alberto Griggio @copyright: 2014-2016 Carsten Grohmann @copyright: 2016-2021 Dietmar Schwertberger @license: MIT (see LICENSE.txt) - THIS PROGRAM COMES WITH NO WARRANTY """ import common, compat import wcodegen def xrc_code_generator(obj): xrcgen = common.code_writers['XRC'] return DatePickerCtrlXrcObject(obj) def initialize(): klass = 'wxDatePickerCtrl' common.class_names['EditDatePickerCtrl'] = klass common.register('python', klass, PythonDatePickerCtrlGenerator(klass)) common.register('C++', klass, CppDatePickerCtrlGenerator(klass)) common.register('XRC', klass, xrc_code_generator)

33.695122

106

0.615635

1642121cd961a12c79b579c9fabd08e8a6ce9bc8

3,960

py

Python

train.py

lck1201/simple-effective-3Dpose-baseline

790a185b44e48a9cc619f52b6615aae729bff76b

[ "MIT" ]

20

2019-03-29T12:20:10.000Z

2021-02-07T08:32:18.000Z

train.py

motokimura/simple-effective-3Dpose-baseline

790a185b44e48a9cc619f52b6615aae729bff76b

[ "MIT" ]

10

2019-04-03T15:25:00.000Z

2021-03-26T16:23:33.000Z

train.py

motokimura/simple-effective-3Dpose-baseline

790a185b44e48a9cc619f52b6615aae729bff76b

[ "MIT" ]

7

2019-06-02T13:25:27.000Z

2020-12-17T06:07:17.000Z

import pprint import mxnet as mx from mxnet import gluon from mxnet import init from lib.core.get_optimizer import * from lib.core.metric import MPJPEMetric from lib.core.loss import MeanSquareLoss from lib.core.loader import JointsDataIter from lib.network import get_net from lib.net_module import * from lib.utils import * from lib.dataset.hm36 import hm36 from config import config, gen_config, update_config_from_args, s_args config = update_config_from_args(config, s_args) if __name__ == '__main__': main()

41.684211

124

0.646212

1643d3915575e537c0423b05a3b3b1e3b7eb7865

6,789

py

Python

FastLinear/generate_memory_bank.py

WangFeng18/dino

1a4e49bd0e99d7e205338b14994a1d57c3084cfe

[ "Apache-2.0" ]

null

FastLinear/generate_memory_bank.py

WangFeng18/dino

1a4e49bd0e99d7e205338b14994a1d57c3084cfe

[ "Apache-2.0" ]

null

FastLinear/generate_memory_bank.py

WangFeng18/dino

1a4e49bd0e99d7e205338b14994a1d57c3084cfe

[ "Apache-2.0" ]

null

import os from tqdm import tqdm import torch.backends.cudnn as cudnn import torch from datasets import ImageNetInstance, ImageNetInstanceLMDB from torchvision import transforms import argparse from BaseTaskModel.task_network import get_moco_network, get_swav_network, get_selfboost_network, get_minmaxent_network, get_simclr_network, get_sup_network, get_dino_network from torch.utils.data import DataLoader from PIL import ImageFile, Image import torch.distributed as dist from lars import * ImageFile.LOAD_TRUNCATED_IMAGES = True import warnings warnings.filterwarnings('ignore') def concat_all_gather(tensor): """ Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. """ tensors_gather = [torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())] torch.distributed.all_gather(tensors_gather, tensor, async_op=False) output = torch.cat(tensors_gather, dim=0) return output if __name__ == '__main__': main()

44.664474

174

0.705259

16447f2400735bc0538f6c77d41578715bdd08b9

2,489

py

Python

tests/utils/test_mercator.py

anuragtr/fabric8-analytics-rudra

13fb15539d195fcb89ced02b205d034ec0c18e00

[ "Apache-2.0" ]

1

2019-05-13T09:31:19.000Z

tests/utils/test_mercator.py

anuragtr/fabric8-analytics-rudra

13fb15539d195fcb89ced02b205d034ec0c18e00

[ "Apache-2.0" ]

null

tests/utils/test_mercator.py

anuragtr/fabric8-analytics-rudra

13fb15539d195fcb89ced02b205d034ec0c18e00

[ "Apache-2.0" ]

null

import pytest from rudra.utils.mercator import SimpleMercator

34.09589

82

0.526718

16449c2c8a80a3f0f14b7a2a74915dc78441651d

139

py

Python

tests/checks/run_performance_tests.py

stjordanis/mljar-supervised

8c3f9d1ed527dfcfdaef91cf82e2779c5832e294

[ "MIT" ]

1,882

2018-11-05T13:20:54.000Z

2022-03-31T14:31:46.000Z

tests/checks/run_performance_tests.py

stjordanis/mljar-supervised

8c3f9d1ed527dfcfdaef91cf82e2779c5832e294

[ "MIT" ]

499

2019-03-14T09:57:51.000Z

2022-03-30T06:00:43.000Z

tests/checks/run_performance_tests.py

stjordanis/mljar-supervised

8c3f9d1ed527dfcfdaef91cf82e2779c5832e294

[ "MIT" ]

277

2019-02-08T21:32:13.000Z

2022-03-29T03:26:05.000Z

import os import sys import unittest from tests.tests_bin_class.test_performance import * if __name__ == "__main__": unittest.main()

15.444444

52

0.769784

1645daef0bb42b38a2691d6bb4f86fefa0af94a5

283

py

Python

task/CheckAllocations.py

wookiee2187/vc3-login-pod

3c0f5490c094bf0b4587a743efac68d722ea5ee2

[ "MIT" ]

1

2019-07-17T19:01:34.000Z

task/CheckAllocations.py

wookiee2187/vc3-login-pod

3c0f5490c094bf0b4587a743efac68d722ea5ee2

[ "MIT" ]

null

task/CheckAllocations.py

wookiee2187/vc3-login-pod

3c0f5490c094bf0b4587a743efac68d722ea5ee2

[ "MIT" ]

null

#!/usr/bin/env python from vc3master.task import VC3Task

16.647059

58

0.590106

16477f8a306c6c85422ce092acee78844c0cd611

4,037

py

Python

django_airbrake/utils/client.py

Captricity/airbrake-django

2ea126653883732a13f1a80c9e567b7076601620

[ "BSD-3-Clause" ]

null

django_airbrake/utils/client.py

Captricity/airbrake-django

2ea126653883732a13f1a80c9e567b7076601620

[ "BSD-3-Clause" ]

2

2016-07-12T15:44:02.000Z

2016-08-19T20:31:49.000Z

django_airbrake/utils/client.py

Captricity/airbrake-django

2ea126653883732a13f1a80c9e567b7076601620

[ "BSD-3-Clause" ]

null

import sys import traceback from django.conf import settings from django.urls import resolve from lxml import etree from six.moves.urllib.request import urlopen, Request

34.211864

107

0.566757

1648b2044844b3d9b645771b179a716a797264e9

599

py

Python

src/spaceone/inventory/connector/snapshot.py

jean1042/plugin-azure-cloud-services

3a75a516c9a4d1e8a4962988934ead3fd40e8494

[ "Apache-2.0" ]

1

2020-12-08T11:59:54.000Z

src/spaceone/inventory/connector/snapshot.py

jean1042/plugin-azure-cloud-services

3a75a516c9a4d1e8a4962988934ead3fd40e8494

[ "Apache-2.0" ]

4

2021-01-26T10:43:37.000Z

2021-12-17T10:13:33.000Z

src/spaceone/inventory/connector/snapshot.py

jean1042/plugin-azure-cloud-services

3a75a516c9a4d1e8a4962988934ead3fd40e8494

[ "Apache-2.0" ]

2

2021-01-13T03:24:05.000Z

2021-01-19T07:25:45.000Z

import logging from spaceone.inventory.libs.connector import AzureConnector from spaceone.inventory.error import * from spaceone.inventory.error.custom import * __all__ = ['SnapshotConnector'] _LOGGER = logging.getLogger(__name__)

28.52381

69

0.721202

1649638736a414c6fde2874636d2e6f9fe9164e4

2,912

py

Python

docs/tutorial/context/app.py

theasylum/wired

6b6a3e83702b18ebb41ca1f94e957bdf7e44986d

[ "MIT" ]

12

2018-07-22T15:40:35.000Z

2020-12-27T21:39:18.000Z

docs/tutorial/context/app.py

theasylum/wired

6b6a3e83702b18ebb41ca1f94e957bdf7e44986d

[ "MIT" ]

36

2019-03-23T13:47:25.000Z

2020-11-28T18:08:14.000Z

docs/tutorial/context/app.py

theasylum/wired

6b6a3e83702b18ebb41ca1f94e957bdf7e44986d

[ "MIT" ]

6

2019-03-23T20:08:57.000Z

2021-06-03T16:52:06.000Z

""" A customer walks into a store. Do the steps to interact with them: - Get *a* (not *the*) greeter - Interact with them Simple wired application: - Settings that say what punctuation to use - Registry - Two factories that says hello, one for the FrenchCustomer context - A default Customer and FrenchCustomer """ from dataclasses import dataclass from wired import ServiceRegistry def setup(settings: Settings) -> ServiceRegistry: # Make the registry registry = ServiceRegistry() # Make the greeter factories, using punctuation from settings punctuation = settings.punctuation # First the default greeter, no context # Register it as a factory using its class for the "key" registry.register_factory(default_greeter_factory, Greeter) # Now the French greeter, using context of FrenchCustomer # Register it as a factory using its class for the "key", but # this time register with a "context" registry.register_factory( french_greeter_factory, Greeter, context=FrenchCustomer ) return registry def greet_customer(registry: ServiceRegistry, customer: Customer) -> str: # A customer comes in, handle the steps in the greeting # as a container. container = registry.create_container() # Get a Greeter using the customer as context. Use the Customer when # generating the greeting. greeter: Greeter = container.get(Greeter, context=customer) greeting = greeter(customer) return greeting def main(): settings = Settings(punctuation='!!') registry = setup(settings) # *** Default Customer # Make a Customer, pass into the "greet_customer" interaction, # then test the result. customer = Customer(name='Mary') assert 'Hello Mary !!' == greet_customer(registry, customer) # *** French Customer # Make a FrenchCustomer, pass into the "greet_customer" interaction, # then test the result. french_customer = FrenchCustomer(name='Henri') assert 'Bonjour Henri !!' == greet_customer(registry, french_customer)

25.54386

74

0.712569

1649bff1d5c282f752cad12fddde82da77d3b6ea

3,133

py

Python

feast/DetectionModules/ldar_program.py

GeoSensorWebLab/FEAST_PtE

63ff8b7925873d756666f3c0c4b9f0f84abd5eb2

[ "MIT" ]

10

2020-03-26T20:12:19.000Z

2022-02-14T22:47:01.000Z

feast/DetectionModules/ldar_program.py

GeoSensorWebLab/FEAST_PtE

63ff8b7925873d756666f3c0c4b9f0f84abd5eb2

[ "MIT" ]

1

2021-07-14T21:14:12.000Z

feast/DetectionModules/ldar_program.py

GeoSensorWebLab/FEAST_PtE

63ff8b7925873d756666f3c0c4b9f0f84abd5eb2

[ "MIT" ]

9

2020-03-27T22:57:31.000Z

2021-09-29T17:29:35.000Z

""" This module defines the LDARProgram class. """ import numpy as np import copy from .repair import Repair from ..EmissionSimModules.result_classes import ResultDiscrete, ResultContinuous

48.2

120

0.679221

164cf23737de25e42e24acaa15cc12f759dc3323

12,783

py

Python

src/CycleGAN.py

sjmoran/SIDGAN

169bd69974bbb7f5760c28a00c231a856017e51c

[ "0BSD" ]

25

2020-09-17T06:29:41.000Z

2022-03-22T06:38:37.000Z

src/CycleGAN.py

sjmoran/SIDGAN

169bd69974bbb7f5760c28a00c231a856017e51c

[ "0BSD" ]

2

2021-05-30T09:00:46.000Z

2021-11-24T08:34:26.000Z

src/CycleGAN.py

sjmoran/SIDGAN

169bd69974bbb7f5760c28a00c231a856017e51c

[ "0BSD" ]

5

2020-10-16T00:44:10.000Z

2021-11-04T15:59:55.000Z

#Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. #This program is free software; you can redistribute it and/or modify it under the terms of the BSD 0-Clause License. #This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the BSD 0-Clause License for more details. from keras.optimizers import Adam from models.ICCV_architectures import * from models.unet import * from keras.engine.topology import Network import sys import tensorflow as tf from utilities.data_loader import *

46.824176

181

0.586013

164e763a74e067d7e8c03c1d5ec3635ec5b33a02

876

py

Python

application/fastapi/main.py

edson-dev/neoway

f792e16c0f627e8b94b54f001e87e076f36311ab

[ "MIT" ]

null

application/fastapi/main.py

edson-dev/neoway

f792e16c0f627e8b94b54f001e87e076f36311ab

[ "MIT" ]

null

application/fastapi/main.py

edson-dev/neoway

f792e16c0f627e8b94b54f001e87e076f36311ab

[ "MIT" ]

null

import uvicorn from fastapi import FastAPI from fastapi.staticfiles import StaticFiles from routes import doc, api from fastapi.templating import Jinja2Templates from starlette.requests import Request # configure static and templates file on jinja 2 app = FastAPI( title=f"Technical Case", description=f"endpoint para subir planilhas para banco de dados relacional Postgres.", version=f"0.0.1", static_directory="static" ) app.mount("/static", StaticFiles(directory="static"), name="static") #import factory builders and initiate doc.init_app(app) api.init_app(app, "/api") # templates = Jinja2Templates(directory="templates") #views if __name__ == "__main__": uvicorn.run("main:app", host="0.0.0.0", port=8080)

28.258065

90

0.745434

164f24393208739c6bb0a99eb1b2e8ed9fcd90d3

58,056

py

Python

civis/io/_tables.py

jsfalk/civis-python

39b6498b2d67d838d720d9631d74f3d3d43f7c1a

[ "BSD-3-Clause" ]

null

civis/io/_tables.py

jsfalk/civis-python

39b6498b2d67d838d720d9631d74f3d3d43f7c1a

[ "BSD-3-Clause" ]

null

civis/io/_tables.py

jsfalk/civis-python

39b6498b2d67d838d720d9631d74f3d3d43f7c1a

[ "BSD-3-Clause" ]

null

import json import concurrent.futures import csv from os import path import io import logging import os import shutil from tempfile import TemporaryDirectory import warnings import zlib import gzip import zipfile from civis import APIClient from civis._utils import maybe_get_random_name from civis.base import EmptyResultError, CivisImportError from civis.futures import CivisFuture from civis.io import civis_to_file, file_to_civis, query_civis from civis.utils import run_job from civis._deprecation import deprecate_param import requests try: from io import StringIO except ImportError: from cStringIO import StringIO try: import pandas as pd NO_PANDAS = False except ImportError: NO_PANDAS = True CHUNK_SIZE = 32 * 1024 log = logging.getLogger(__name__) __all__ = ['read_civis', 'read_civis_sql', 'civis_to_csv', 'civis_to_multifile_csv', 'dataframe_to_civis', 'csv_to_civis', 'civis_file_to_table', 'split_schema_tablename', 'export_to_civis_file'] DELIMITERS = { ',': 'comma', '\t': 'tab', '|': 'pipe', } def export_to_civis_file(sql, database, job_name=None, client=None, credential_id=None, polling_interval=None, hidden=True, csv_settings=None): """Store results of a query to a Civis file Parameters ---------- sql : str The SQL select string to be executed. database : str or int Execute the query against this database. Can be the database name or ID. job_name : str, optional A name to give the job. If omitted, a random job name will be used. client : :class:`civis.APIClient`, optional If not provided, an :class:`civis.APIClient` object will be created from the :envvar:`CIVIS_API_KEY`. credential_id : str or int, optional The database credential ID. If ``None``, the default credential will be used. polling_interval : int or float, optional Number of seconds to wait between checks for query completion. hidden : bool, optional If ``True`` (the default), this job will not appear in the Civis UI. csv_settings : dict, optional A dictionary of csv_settings to pass to :func:`civis.APIClient.scripts.post_sql`. Returns ------- fut : :class:`~civis.futures.CivisFuture` A future which returns the response from :func:`civis.APIClient.scripts.get_sql_runs` after the sql query has completed and the result has been stored as a Civis file. Examples -------- >>> sql = "SELECT * FROM schema.table" >>> fut = export_to_civis_file(sql, "my_database") >>> file_id = fut.result()['output'][0]["file_id"] See Also -------- civis.io.read_civis : Read directly into memory without SQL. civis.io.read_civis_sql : Read results of a SQL query into memory. civis.io.civis_to_csv : Write directly to a CSV file. civis.io.civis_file_to_table : Upload a Civis file to a Civis table """ client = client or APIClient() script_id, run_id = _sql_script(client=client, sql=sql, database=database, job_name=job_name, credential_id=credential_id, csv_settings=csv_settings, hidden=hidden) fut = CivisFuture(client.scripts.get_sql_runs, (script_id, run_id), polling_interval=polling_interval, client=client, poll_on_creation=False) return fut def _sql_script(client, sql, database, job_name, credential_id, hidden=False, csv_settings=None): job_name = maybe_get_random_name(job_name) db_id = client.get_database_id(database) credential_id = credential_id or client.default_credential csv_settings = csv_settings or {} export_job = client.scripts.post_sql(job_name, remote_host_id=db_id, credential_id=credential_id, sql=sql, hidden=hidden, csv_settings=csv_settings) run_job = client.scripts.post_sql_runs(export_job.id) log.debug('Started run %d of SQL script %d', run_job.id, export_job.id) return export_job.id, run_job.id def _get_sql_select(table, columns=None): if columns and not isinstance(columns, (list, tuple)): raise TypeError("columns must be a list, tuple or None") select = ", ".join(columns) if columns is not None else "*" sql = "select {} from {}".format(select, table) return sql def _get_headers(client, sql, database, credential_id, polling_interval=None): headers = None try: # use 'begin read only;' to ensure we can't change state sql = 'begin read only; select * from ({}) limit 1'.format(sql) fut = query_civis(sql, database, client=client, credential_id=credential_id, polling_interval=polling_interval) headers = fut.result()['result_columns'] except Exception as exc: # NOQA log.debug("Failed to retrieve headers due to %s", str(exc)) return headers def _decompress_stream(response, buf, write_bytes=True): # use response.raw for a more consistent approach # if content-encoding is specified in the headers # then response.iter_content will decompress the stream # however, our use of content-encoding is inconsistent chunk = response.raw.read(CHUNK_SIZE) d = zlib.decompressobj(zlib.MAX_WBITS | 32) while chunk or d.unused_data: if d.unused_data: to_decompress = d.unused_data + chunk d = zlib.decompressobj(zlib.MAX_WBITS | 32) else: to_decompress = d.unconsumed_tail + chunk if write_bytes: buf.write(d.decompress(to_decompress)) else: buf.write(d.decompress(to_decompress).decode('utf-8')) chunk = response.raw.read(CHUNK_SIZE) def split_schema_tablename(table): """Split a Redshift 'schema.tablename' string Remember that special characters (such as '.') can only be included in a schema or table name if delimited by double-quotes. Parameters ---------- table: str Either a Redshift schema and table name combined with a ".", or else a single table name. Returns ------- schema, tablename A 2-tuple of strings. The ``schema`` may be None if the input is only a table name, but the ``tablename`` will always be filled. Raises ------ ValueError If the input ``table`` is not separable into a schema and table name. """ reader = csv.reader(StringIO(str(table)), delimiter=".", doublequote=True, quotechar='"') schema_name_tup = next(reader) if len(schema_name_tup) == 1: schema_name_tup = (None, schema_name_tup[0]) if len(schema_name_tup) != 2: raise ValueError("Cannot parse schema and table. " "Does '{}' follow the pattern 'schema.table'?" .format(table)) return tuple(schema_name_tup) def _replace_null_column_names(column_list): """Replace null names in columns from file cleaning with an appropriately blank column name. Parameters ---------- column_list: list[dict] the list of columns from file cleaning. Returns -------- column_list: list[dict] """ new_cols = [] for i, col in enumerate(column_list): # Avoid mutating input arguments new_col = dict(col) if new_col.get('name') is None: new_col['name'] = 'column_{}'.format(i) new_cols.append(new_col) return new_cols def _check_all_detected_info(detected_info, headers, delimiter, compression, output_file_id): """Check a single round of cleaning results as compared to provided values. Parameters ---------- detected_info: Dict[str, Any] The detected info of the file as returned by the Civis API. headers: bool The provided value for whether or not the file contains errors. delimiter: str The provided value for the file delimiter. compression: str The provided value for the file compression. output_file_id: int The cleaned file's Civis ID. Used for debugging. Raises ------ CivisImportError If the values detected on the file do not match their expected attributes. """ if headers != detected_info['includeHeader']: raise CivisImportError('Mismatch between detected headers - ' 'please ensure all imported files either ' 'have a header or do not.') if delimiter != detected_info['columnDelimiter']: raise CivisImportError('Provided delimiter "{}" does not match ' 'detected delimiter for {}: "{}"'.format( delimiter, output_file_id, detected_info["columnDelimiter"]) ) if compression != detected_info['compression']: raise CivisImportError('Mismatch between detected and provided ' 'compressions - provided compression was {}' ' but detected compression {}. Please ' 'ensure all imported files have the same ' 'compression.'.format( compression, detected_info['compression']) ) def _check_column_types(table_columns, file_columns, output_obj_id): """Check that base column types match those current defined for the table. Parameters ---------- table_columns: List[Dict[str, str]] The columns for the table to be created. file_columns: List[Dict[str, str]] The columns detected by the Civis API for the file. output_obj_id: int The file ID under consideration; used for error messaging. Raises ------ CivisImportError If the table columns and the file columns have a type mismatch, or differ in count. """ if len(table_columns) != len(file_columns): raise CivisImportError('All files should have the same number of ' 'columns. Expected {} columns but file {} ' 'has {} columns'.format( len(table_columns), output_obj_id, len(file_columns)) ) error_msgs = [] for idx, (tcol, fcol) in enumerate(zip(table_columns, file_columns)): # for the purposes of type checking, we care only that the types # share a base type (e.g. INT, VARCHAR, DECIMAl) rather than that # they have the same precision and length # (e.g VARCHAR(42), DECIMAL(8, 10)) tcol_base_type = tcol['sql_type'].split('(', 1)[0] fcol_base_type = fcol['sql_type'].split('(', 1)[0] if tcol_base_type != fcol_base_type: error_msgs.append( 'Column {}: File base type was {}, but expected {}'.format( idx, fcol_base_type, tcol_base_type ) ) if error_msgs: raise CivisImportError( 'Encountered the following errors for file {}:\n\t{}'.format( output_obj_id, '\n\t'.join(error_msgs) ) )

40.798313

79

0.617111

164f6ae0c583900eea5f44762f6006a785208240

2,218

py

Python

tests/unit/small_text/integrations/pytorch/test_strategies.py

chschroeder/small-text

ef28e91ba0c94fe938dde4f16253aa8695ea13b7

[ "MIT" ]

218

2021-05-26T16:38:53.000Z

2022-03-30T09:48:54.000Z

tests/unit/small_text/integrations/pytorch/test_strategies.py

chschroeder/small-text

ef28e91ba0c94fe938dde4f16253aa8695ea13b7

[ "MIT" ]

9

2021-10-16T23:23:02.000Z

2022-02-22T15:23:11.000Z

tests/unit/small_text/integrations/pytorch/test_strategies.py

chschroeder/small-text

ef28e91ba0c94fe938dde4f16253aa8695ea13b7

[ "MIT" ]

21

2021-06-24T11:19:44.000Z

2022-03-12T16:29:53.000Z

import unittest import pytest from small_text.integrations.pytorch.exceptions import PytorchNotFoundError try: from small_text.integrations.pytorch.query_strategies import ( BADGE, ExpectedGradientLength, ExpectedGradientLengthMaxWord) except PytorchNotFoundError: pass

30.383562

94

0.712353

164ff194ddd6475fcc83a8af8f5b4d32701c55ea

886

py

Python

pymterm/colour/tango.py

stonewell/pymterm

af36656d5f7fb008533178d14b00d83d72ba00cf

[ "MIT" ]

102

2016-07-21T06:39:02.000Z

2022-03-09T19:34:03.000Z

pymterm/colour/tango.py

stonewell/pymterm

af36656d5f7fb008533178d14b00d83d72ba00cf

[ "MIT" ]

2

2017-01-11T13:43:34.000Z

2020-01-19T12:06:47.000Z

pymterm/colour/tango.py

stonewell/pymterm

af36656d5f7fb008533178d14b00d83d72ba00cf

[ "MIT" ]

4

2020-03-22T04:08:35.000Z

2021-06-27T23:38:02.000Z

TANGO_PALLETE = [ '2e2e34343636', 'cccc00000000', '4e4e9a9a0606', 'c4c4a0a00000', '34346565a4a4', '757550507b7b', '060698989a9a', 'd3d3d7d7cfcf', '555557575353', 'efef29292929', '8a8ae2e23434', 'fcfce9e94f4f', '72729f9fcfcf', 'adad7f7fa8a8', '3434e2e2e2e2', 'eeeeeeeeecec', ]

24.611111

69

0.613995

16506683fe35155169d6fbcd3b4087bff7394386

22,681

py

Python

user_manager/oauth/oauth2.py

voegtlel/auth-manager-backend

20d40de0abc9deeb3fcddd892ffe2e635301917a

[ "MIT" ]

null

user_manager/oauth/oauth2.py

voegtlel/auth-manager-backend

20d40de0abc9deeb3fcddd892ffe2e635301917a

[ "MIT" ]

null

user_manager/oauth/oauth2.py

voegtlel/auth-manager-backend

20d40de0abc9deeb3fcddd892ffe2e635301917a

[ "MIT" ]

null

from datetime import datetime, timedelta from enum import Enum from typing import List, Optional, Tuple, Dict, Any, Union import time from authlib.common.security import generate_token from authlib.consts import default_json_headers from authlib.oauth2 import ( OAuth2Request, AuthorizationServer as _AuthorizationServer, ResourceProtector as _ResourceProtector, OAuth2Error, HttpRequest, ) from authlib.oauth2.rfc6749 import InvalidClientError from authlib.oauth2.rfc6749.grants import ( AuthorizationCodeGrant as _AuthorizationCodeGrant, RefreshTokenGrant as _RefreshTokenGrant, BaseGrant, ) from authlib.oauth2.rfc6749.grants import ( ResourceOwnerPasswordCredentialsGrant as _ResourceOwnerPasswordCredentialsGrant, ) from authlib.oauth2.rfc6749.util import scope_to_list from authlib.oauth2.rfc6750 import BearerTokenValidator as _BearerTokenValidator, BearerToken as _BearerToken, \ InsufficientScopeError from authlib.oauth2.rfc8414 import AuthorizationServerMetadata from authlib.oidc.core import UserInfo from authlib.oidc.core.grants import ( OpenIDCode as _OpenIDCode, OpenIDImplicitGrant as _OpenIDImplicitGrant, OpenIDHybridGrant as _OpenIDHybridGrant, ) from authlib.oidc.core.grants.util import is_openid_scope, generate_id_token from fastapi import HTTPException from starlette.concurrency import run_in_threadpool from starlette.responses import Response, JSONResponse from user_manager.common.config import config from user_manager.common.models import DbAuthorizationCode, DbToken, DbClient, DbUser, DbManagerSchema, DbUserProperty, \ UserPropertyType from user_manager.common.mongo import authorization_code_collection, token_collection, \ client_collection, client_user_cache_collection, user_group_collection, async_token_collection, \ async_user_group_collection, async_client_collection, user_collection, read_schema, async_read_schema from . import oauth2_key from .user_helper import UserWithRoles USERS_SCOPE = '*users' def save_authorization_code(code: str, request: TypedRequest): nonce = request.data.get('nonce') item = DbAuthorizationCode( code=code, client_id=request.client.id, redirect_uri=request.redirect_uri, scope=request.scope, user_id=request.user.user.id, nonce=nonce, auth_time=int(time.time()), expiration_time=datetime.utcnow() + timedelta(seconds=config.oauth2.token_expiration.authorization_code), ) authorization_code_collection.insert_one(item.document()) return item def save_token(token: Dict[str, Any], request: TypedRequest): if request.user: user_id = request.user.user.id else: user_id = None now = int(time.time()) token_data = DbToken.validate_document({ 'client_id': request.client.id, 'user_id': user_id, 'issued_at': now, 'expiration_time': datetime.utcnow() + timedelta(seconds=token.get('expires_in', 0)), 'scope': request.scope, 'auth_time': request.credential.get_auth_time(), **token }) token_collection.insert_one(token_data.document()) return token_data def query_client(client_id: str): client_data = client_collection.find_one({'_id': client_id}) if client_data is None: return None return DbClient.validate_document(client_data) authorization = AuthorizationServer( query_client, save_token, BearerToken(AccessTokenGenerator(), expires_generator=token_expires_in, refresh_token_generator=token_generator), ) # support all openid grants authorization.register_grant(AuthorizationCodeGrant, [OpenIDCode(), OpenIDSessionState()]) authorization.register_grant(OpenIDImplicitGrant) authorization.register_grant(OpenIDHybridGrant) authorization.register_grant(RefreshTokenGrant, [OpenIDCode(), OpenIDSessionState()]) authorization.register_grant(ResourceOwnerPasswordCredentialsGrant) resource_protector = ResourceProtector() resource_protector.register_token_validator(BearerTokenValidator()) user_introspection = UserIntrospection() token_revocation = RevocationEndpoint() request_origin_verifier = RequestOriginVerifier() other_user_inspection = OtherUserInspection() other_users_inspection = OtherUsersInspection()

40.501786

121

0.680217

16517f3c2ccf47bb7eb0759cee7e8d2e4ec1a86f

3,553

py

Python

src/adsb/sbs/server.py

claws/adsb

4a7d35880dece6baaf24370fab445e2571fc19e9

[ "MIT" ]

7

2018-07-11T00:50:47.000Z

2021-09-29T10:36:44.000Z

src/adsb/sbs/server.py

claws/adsb

4a7d35880dece6baaf24370fab445e2571fc19e9

[ "MIT" ]

3

2020-06-13T23:27:42.000Z

2020-07-22T03:06:16.000Z

src/adsb/sbs/server.py

claws/adsb

4a7d35880dece6baaf24370fab445e2571fc19e9

[ "MIT" ]

3

2020-01-08T19:05:42.000Z

2022-02-11T02:22:23.000Z

import asyncio import datetime import logging import socket from . import protocol from typing import Tuple from asyncio import AbstractEventLoop logger = logging.getLogger(__name__) def deregister_protocol(self, peer: Tuple[str, int]) -> None: """ De-register a protocol instance from the server. This peer will no longer receive messages. :param peer: Tuple of (host:str, port:int). """ del self.protocols[peer] def send_message(self, msg: bytes, peer: Tuple[str, int] = None) -> None: """ Send a message. :param msg: A bytes object representing the SBS format message to send to peers. The message is assumed to include the end of message delimiter. :param peer: A specific peer to send the message to. Peer is a Tuple of (host:str, port:int). If not specified then the message is broadcast to all peers. """ if self.protocols: if peer: prot = self.protocols.get(peer) if prot: prot.send_message(msg) else: raise Exception( f"Server can't send msg to non-existant peer: {peer}" ) else: # broadcast message to all peers for peer, prot in self.protocols.items(): prot.send_message(msg) else: raise Exception("Server can't send msg, no peers available")

32.59633

77

0.565156

1652c769892c847b99d4a49f23694f814ea670c4

2,803

py

Python

src/robusta/core/model/events.py

kandahk/robusta

61a2001cb1c4e90e8a74b810463ec99e6cb80787

[ "MIT" ]

null

src/robusta/core/model/events.py

kandahk/robusta

61a2001cb1c4e90e8a74b810463ec99e6cb80787

[ "MIT" ]

null

src/robusta/core/model/events.py

kandahk/robusta

61a2001cb1c4e90e8a74b810463ec99e6cb80787

[ "MIT" ]

null

import logging import uuid from enum import Enum from typing import List, Optional, Dict, Any from dataclasses import dataclass, field from pydantic import BaseModel from ...integrations.scheduled.playbook_scheduler import PlaybooksScheduler from ..reporting.base import Finding, BaseBlock # Right now: # 1. this is a dataclass but we need to make all fields optional in subclasses because of https://stackoverflow.com/questions/51575931/ # 2. this can't be a pydantic BaseModel because of various pydantic bugs (see https://github.com/samuelcolvin/pydantic/pull/2557) # once the pydantic PR that addresses those issues is merged, this should be a pydantic class # (note that we need to integrate with dataclasses because of hikaru)

35.481013

135

0.708883

1653cd2fffd32e2ad6ea59e14f67f33d48afc170

560

py

Python

examples/django_mongoengine/bike/models.py

pfrantz/graphene-mongo

f7d4f3e194ec41793e6da547934c34e11fd9ef51

[ "MIT" ]

260

2018-02-03T01:00:42.000Z

2022-02-18T12:42:01.000Z

examples/django_mongoengine/bike/models.py

pfrantz/graphene-mongo

f7d4f3e194ec41793e6da547934c34e11fd9ef51

[ "MIT" ]

159

2018-02-09T07:35:03.000Z

2022-03-20T03:43:23.000Z

examples/django_mongoengine/bike/models.py

pfrantz/graphene-mongo

f7d4f3e194ec41793e6da547934c34e11fd9ef51

[ "MIT" ]

124

2018-02-04T20:19:01.000Z

2022-03-25T21:40:41.000Z

from mongoengine import Document from mongoengine.fields import ( FloatField, StringField, ListField, URLField, ObjectIdField, )

20

35

0.642857

1653e68a3494182dbc33ba8410b68bb9f85c16c2

97

py

Python

src/tensor/tensor/movement/__init__.py

jedhsu/tensor

3b2fe21029fa7c50b034190e77d79d1a94ea5e8f

[ "Apache-2.0" ]

null

src/tensor/tensor/movement/__init__.py

jedhsu/tensor

3b2fe21029fa7c50b034190e77d79d1a94ea5e8f

[ "Apache-2.0" ]

null

src/tensor/tensor/movement/__init__.py

jedhsu/tensor

3b2fe21029fa7c50b034190e77d79d1a94ea5e8f

[ "Apache-2.0" ]

null

from ._movement import Movement from .path import MovementPath from .paths import MovementPaths

19.4

32

0.835052

1654499e8423c0c8a91eb13123406b32dfc847c1

8,988

py

Python

opticalmapping/standalone/om_augmenter.py

sauloal/ipython

35c24a10330da3e54b5ee29df54ee263f5268d18

[ "MIT" ]

null

opticalmapping/standalone/om_augmenter.py

sauloal/ipython

35c24a10330da3e54b5ee29df54ee263f5268d18

[ "MIT" ]

null

opticalmapping/standalone/om_augmenter.py

sauloal/ipython

35c24a10330da3e54b5ee29df54ee263f5268d18

[ "MIT" ]

null

#!/usr/bin/python import os import sys from om_shared import * if __name__ == '__main__': if len(sys.argv) ==1: print "no arguments given" sys.exit(1) args = parse_args(sys.argv[1:]) main(args) """ # $ cd D:\Plextor\data\Acquisitie\BioNanoGenomics\MyLycopersicumWorkspace_31022015\Imports; C:\Program Files\BioNano Genomics\RefAligner\WindowsRefAligner.exe -f -ref D:\Plextor\data\Acquisitie\BioNanoGenomics\MyLycopersicumWorkspace_31022015\Imports\S_lycopersicum_chromosomes.2.50.BspQI-BbvCI.cmap -i D:\Plextor\data\Acquisitie\BioNanoGenomics\MyLycopersicumWorkspace_31022015\Imports\EXP_REFINEFINAL1.cmap -o S_lycopersicum_chromosomes.2.50.BspQI-BbvCI_to_EXP_REFINEFINAL1 -endoutlier 1e-2 -outlier 1e-4 -extend 1 -FN 0.08 -FP 0.8 -sf 0.2 -sd 0 -sr 0.02 -res 2.9 -resSD 0.7 -mres 2.0 -A 5 -biaswt 0 -M 1 -Mfast 0 -maxmem 2 -T 1e-6 -stdout -stderr # r3498 $Header: http://svn.bnm.local:81/svn/informatics/RefAligner/branches/3480/RefAligner.cpp 3470 2014-12-17 19:29:21Z tanantharaman $ # FLAGS: USE_SSE=0 USE_AVX=0 USE_MIC=0 USE_PFLOAT=1 USE_RFLOAT=1 DEBUG=1 VERB=1 # XMAP File Version: 0.2 # Label Channels: 1 # Reference Maps From: S_lycopersicum_chromosomes.2.50.BspQI-BbvCI_to_EXP_REFINEFINAL1_r.cmap # Query Maps From: S_lycopersicum_chromosomes.2.50.BspQI-BbvCI_to_EXP_REFINEFINAL1_q.cmap #h XmapEntryID QryContigID RefContigID QryStartPos QryEndPos RefStartPos RefEndPos Orientation Confidence HitEnum QryLen RefLen LabelChannel Alignment #f int int int float float float float string float string float float int string 1 141 1 528400.6 571697.5 10672 54237.5 + 6.65 4M2D2M 1439123.5 21805821 1 "(1,34)(2,34)(3,35)(4,36)(5,37)(6,38)(8,38)(9,39)" 2 174 1 21236.5 1568390 10672 1553561 + 79.35 2M3D1M1D1M1D4M1I2M1D2M1D1M2I2D9M3I3M1D6M1D2M2D1M1D6M1D1M1D1M2D2M2D1M1I1D1M1D5M2D4M2D1M2D2M1D2M1D3M1D1M1D2M3I3D1M1D1M3D2M3D1M2I1D1M2D1M1D1M1I2D3M2I1M1D2M1D1M1D1M2I3D3M3D1M2D1M1D1M1D5M2D12M 1568410 21805821 1 "(1,2)(2,2)(3,3)(6,4)(7,4)(9,5)(11,6)(12,7)(13,8)(14,9)(15,11)(16,12)(18,13)(19,14)(20,15)(21,15)(24,18)(25,19)(26,20)(27,21)(28,22)(29,23)(30,24)(31,25)(32,26)(33,30)(34,31)(35,32)(37,33)(38,34)(39,35)(40,36)(41,37)(42,38)(44,39)(45,40)(47,41)(48,41)(50,42)(51,43)(52,44)(53,45)(54,46)(55,47)(57,48)(59,49)(60,50)(62,50)(63,51)(66,52)(68,54)(69,55)(70,55)(71,56)(72,57)(73,58)(74,59)(76,60)(77,60)(78,61)(79,62)(80,63)(82,64)(83,64)(86,65)(87,66)(89,67)(90,68)(92,69)(93,70)(94,71)(95,72)(96,72)(98,73)(99,74)(103,78)(105,79)(109,80)(110,81)(111,82)(114,82)(116,85)(119,86)(120,87)(121,87)(124,89)(125,90)(126,91)(127,94)(128,95)(129,95)(130,96)(132,97)(134,98)(138,101)(139,102)(140,103)(143,104)(144,104)(146,105)(147,105)(149,106)(151,107)(152,108)(153,109)(154,110)(155,111)(158,112)(159,113)(160,114)(161,115)(162,116)(163,117)(164,118)(165,119)(166,120)(167,121)(168,122)(169,123)" """

61.561644

1,184

0.595683

1654fce2866f6b2ef021c29092efa26419e5ba83

4,918

py

Python

uhd_restpy/testplatform/sessions/ixnetwork/impairment/profile/fixedclassifier/fixedclassifier.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

20

2019-05-07T01:59:14.000Z

2022-02-11T05:24:47.000Z

uhd_restpy/testplatform/sessions/ixnetwork/impairment/profile/fixedclassifier/fixedclassifier.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

60

2019-04-03T18:59:35.000Z

2022-02-22T12:05:05.000Z

uhd_restpy/testplatform/sessions/ixnetwork/impairment/profile/fixedclassifier/fixedclassifier.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

13

2019-05-20T10:48:31.000Z

2021-10-06T07:45:44.000Z

# MIT LICENSE # # Copyright 1997 - 2020 by IXIA Keysight # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. from uhd_restpy.base import Base from uhd_restpy.files import Files from typing import List, Any, Union

41.677966

187

0.700895

16557fb191c1ea62849d52d444fde47864d855b9

43,651

py

Python

lantz/drivers/sacher/Sacher_EPOS.py

mtsolmn/lantz-drivers

f48caf9000ddd08f2abb837d832e341410af4788

[ "BSD-3-Clause" ]

4

2019-05-04T00:10:53.000Z

2020-10-22T18:08:40.000Z

lantz/drivers/sacher/Sacher_EPOS.py

mtsolmn/lantz-drivers

f48caf9000ddd08f2abb837d832e341410af4788

[ "BSD-3-Clause" ]

3

2019-07-12T13:44:17.000Z

2020-10-22T19:32:08.000Z

lantz/drivers/sacher/Sacher_EPOS.py

mtsolmn/lantz-drivers

f48caf9000ddd08f2abb837d832e341410af4788

[ "BSD-3-Clause" ]

9

2019-04-03T17:07:03.000Z

2021-02-15T21:53:55.000Z

# sacher_epos.py, python wrapper for sacher epos motor # David Christle <[email protected]>, August 2014 # """ Possbily Maxon EPOS now """ """ This is the actual version that works But only in the lab32 virtual environment """ # from instrument import Instrument # import qt import ctypes import ctypes.wintypes import logging import time # from instrument import Instrument from ctypes.wintypes import DWORD, WORD import numpy as np """ okay so we import a bunch of random stuff I always forget what ctypes is for but I'll worry about it later """ # from subprocess import Popen, PIPE # from multiprocessing.managers import BaseManager # import atexit # import os # python32_dir = "C:\\Users\\Alex\\Miniconda3\\envs\\lab32" # assert os.path.isdir(python32_dir) # os.chdir(python32_dir) # derp = "C:\\Users\\Alex\\Documents\\wow_such_code" # assert os.path.isdir(derp) # os.chdir(derp) # p = Popen([python32_dir + "\\python.exe", derp + "\\delegate.py"], stdout=PIPE, cwd=derp) # atexit.register(p.terminate) # port = int(p.stdout.readline()) # authkey = p.stdout.read() # print(port, authkey) # m = BaseManager(address=("localhost", port), authkey=authkey) # m.connect() # tell manager to expect an attribute called LibC # m.register("SacherLasaTeknique") # access and use libc # libc = m.SacherLasaTeknique() # print(libc.vcs()) # eposlib = ctypes.windll.eposcmd eposlib = ctypes.windll.LoadLibrary('C:\\Users\\Carbro\\Desktop\\Charmander\\EposCmd.dll') DeviceName = b'EPOS' ProtocolStackName = b'MAXON_RS232' InterfaceName = b'RS232' """ Max on Max off but anyway it looks like ctypes is the thing that's talking to the epos dll """ HISTCHAN = 65536 TTREADMAX = 131072 RANGES = 8 MODE_HIST = 0 MODE_T2 = 2 MODE_T3 = 3 FLAG_OVERFLOW = 0x0040 FLAG_FIFOFULL = 0x0003 # in mV ZCMIN = 0 ZCMAX = 20 DISCRMIN = 0 DISCRMAX = 800 # in ps OFFSETMIN = 0 OFFSETMAX = 1000000000 # in ms ACQTMIN = 1 ACQTMAX = 10 * 60 * 60 * 1000 # in mV PHR800LVMIN = -1600 PHR800LVMAX = 2400 """ wooooooo a bunch a variables and none of them are explained way to go dc you da real champ """ """ Also we're done with the Sacher_EPOS() class at this point """ if __name__ == '__main__': epos = Sacher_EPOS(None, b'COM3') # epos.set_coeffs(8.34529e-12,8.49218e-5,1081.92,10840,11860) # epos.do_get_wavelength() # print('#1 Motor current: {}'.format(epos.get_motor_current())) # epos.do_get_wavelength() # print('motor position is...') # current_pos = epos.get_motor_position() # print('current position is {}'.format(current_pos)) # new_pos = current_pos + 10000 # epos.set_target_position(new_pos, True, True) # print(epos.get_motor_position()) # print('#2 Motor current: {}'.format(epos.get_motor_current())) # epos.find_home() # epos.restore() # time.sleep(7) epos.do_set_wavelength(1151.5) # epos.do_get_wavelength() print('Motor current: {}'.format(epos.get_motor_current())) print('Motor position: {}'.format(epos.get_motor_position())) """ OTHER MISC. NOTES: increasing wavelength: causes the square to rotate left causes base to move to the left when square is stuck in causes screw to loosen causes large gold base to tighten decreasing wavelength: there's an overshoot when lowering wavelength causes the square to rotate right causes base to move to the right when square is stuck in causes screw to tighten causes large gold base to loosen, and also unplug the motor Also you don't need to explicitly run epos.initialize() because there's an __init__ function which contains epos.initialize() """ # womp the end

41.532826

147

0.625644

165616f6329f47d7fc22c8cc1eb0970f40d768d9

1,652

py

Python

tools/generate_lst.py

haotianliu001/HRNet-Lesion

9dae108879456e084b2200e39d7e58c1c08c2b16

[ "MIT" ]

null

tools/generate_lst.py

haotianliu001/HRNet-Lesion

9dae108879456e084b2200e39d7e58c1c08c2b16

[ "MIT" ]

null

tools/generate_lst.py

haotianliu001/HRNet-Lesion

9dae108879456e084b2200e39d7e58c1c08c2b16

[ "MIT" ]

null

import argparse import os image_dir = 'image' label_dir = 'label' splits = ['train', 'val', 'test'] image_dirs = [ 'image/{}', 'image/{}_crop' ] label_dirs = [ 'label/{}/annotations', 'label/{}/annotations_crop', ] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('root', type=str, help='path of dataset root') args = parser.parse_args() generate(args.root)

30.036364

116

0.579903

1658161ce6f6978b51d0a1fdd4a0ce93c2160124

897

py

Python

examples/example.py

f-dangel/unfoldNd

63e9abc4867d8678c2ac00da567dc106e9f6f2c7

[ "MIT" ]

21

2021-03-04T04:56:20.000Z

2022-03-31T11:15:28.000Z

examples/example.py

f-dangel/unfoldNd

63e9abc4867d8678c2ac00da567dc106e9f6f2c7

[ "MIT" ]

12

2021-02-16T16:16:23.000Z

2021-05-28T06:00:41.000Z

examples/example.py

f-dangel/unfoldNd

63e9abc4867d8678c2ac00da567dc106e9f6f2c7

[ "MIT" ]

1

2021-11-04T12:52:19.000Z

"""How to use ``unfoldNd``. A comparison with ``torch.nn.Unfold``.""" # imports, make this example deterministic import torch import unfoldNd torch.manual_seed(0) # random batched RGB 32x32 image-shaped input tensor of batch size 64 inputs = torch.randn((64, 3, 32, 32)) # module hyperparameters kernel_size = 3 dilation = 1 padding = 1 stride = 2 # both modules accept the same arguments and perform the same operation torch_module = torch.nn.Unfold( kernel_size, dilation=dilation, padding=padding, stride=stride ) lib_module = unfoldNd.UnfoldNd( kernel_size, dilation=dilation, padding=padding, stride=stride ) # forward pass torch_outputs = torch_module(inputs) lib_outputs = lib_module(inputs) # check if torch.allclose(torch_outputs, lib_outputs): print(" Outputs of torch.nn.Unfold and unfoldNd.UnfoldNd match.") else: raise AssertionError(" Outputs don't match")

24.916667

71

0.753623

1658fa9a24f0d70843df0f950d0081f1ffadc11b

797

py

Python

src/pretix/helpers/escapejson.py

NicsTr/pretix

e6d2380d9ed1836cc64a688b2be20d00a8500eab

[ "ECL-2.0", "Apache-2.0" ]

1

2020-04-25T00:11:00.000Z

src/pretix/helpers/escapejson.py

NicsTr/pretix

e6d2380d9ed1836cc64a688b2be20d00a8500eab

[ "ECL-2.0", "Apache-2.0" ]

null

src/pretix/helpers/escapejson.py

NicsTr/pretix

e6d2380d9ed1836cc64a688b2be20d00a8500eab

[ "ECL-2.0", "Apache-2.0" ]

null

from django.utils.encoding import force_str from django.utils.functional import keep_lazy from django.utils.safestring import SafeText, mark_safe _json_escapes = { ord('>'): '\\u003E', ord('<'): '\\u003C', ord('&'): '\\u0026', } _json_escapes_attr = { ord('>'): '\\u003E', ord('<'): '\\u003C', ord('&'): '\\u0026', ord('"'): '"', ord("'"): ''', ord("="): '=', }

25.709677

75

0.6399

1659ed45e2efb246708ee177c0a31eb71473cb9b

1,813

py

Python

pyxley/charts/plotly/base.py

snowind/pyxley

cff9e50b8d80b9794c6907355e541f166959cd6c

[ "MIT" ]

2,536

2015-06-26T20:12:30.000Z

2022-03-01T07:26:44.000Z

pyxley/charts/plotly/base.py

zhiaozhou/pyxley

2dab00022d977d986169cd8a629b3a2f91be893f

[ "MIT" ]

51

2015-07-17T14:16:43.000Z

2021-07-09T21:34:36.000Z

pyxley/charts/plotly/base.py

zhiaozhou/pyxley

2dab00022d977d986169cd8a629b3a2f91be893f

[ "MIT" ]

335

2015-07-16T20:22:00.000Z

2022-02-25T07:18:15.000Z

from ..charts import Chart from flask import jsonify, request _BASE_CONFIG = { "showLink": False, "displaylogo": False, "modeBarButtonsToRemove": ["sendDataToCloud"] }

27.059701

73

0.492554

165b5afa3e28ca226423cdaac8f6894170030430

576

py

Python

pyqt/getting_started/close_window.py

CospanDesign/python

9f911509aae7abd9237c14a4635294c7719c9129

[ "MIT" ]

5

2015-12-12T20:16:45.000Z

2020-02-21T19:50:31.000Z

pyqt/getting_started/close_window.py

CospanDesign/python

9f911509aae7abd9237c14a4635294c7719c9129

[ "MIT" ]

null

pyqt/getting_started/close_window.py

CospanDesign/python

9f911509aae7abd9237c14a4635294c7719c9129

[ "MIT" ]

2

2020-06-01T06:27:06.000Z

2022-03-10T13:21:03.000Z

#!/usr/bin/python import sys from PyQt4 import QtGui from PyQt4 import QtCore if __name__ == "__main__": main()

19.2

65

0.682292

165bd59707bf7d41b2fcb3dbf5d490a2e8660a09

732

py

Python

test/means/test_zero_mean.py

bdecost/gpytorch

a5f1ad3e47daf3f8db04b605fb13ff3f9f871e3a

[ "MIT" ]

null

test/means/test_zero_mean.py

bdecost/gpytorch

a5f1ad3e47daf3f8db04b605fb13ff3f9f871e3a

[ "MIT" ]

null

test/means/test_zero_mean.py

bdecost/gpytorch

a5f1ad3e47daf3f8db04b605fb13ff3f9f871e3a

[ "MIT" ]

1

2018-11-15T10:03:40.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import torch import unittest from gpytorch.means import ZeroMean

28.153846

78

0.629781

165bdb25d95d9e2ecf502312358485ebe1274976

1,948

py

Python

generator/contact.py

rizzak/python_training

38bbe5d7e38892e8dcc28caeae1481b98cce7356

[ "Apache-2.0" ]

null

generator/contact.py

rizzak/python_training

38bbe5d7e38892e8dcc28caeae1481b98cce7356

[ "Apache-2.0" ]

null

generator/contact.py

rizzak/python_training

38bbe5d7e38892e8dcc28caeae1481b98cce7356

[ "Apache-2.0" ]

null

import jsonpickle import random import string from model.contact import Contact import os.path import getopt import sys try: opts, args = getopt.getopt(sys.argv[1:], "n:f:", ["number of contacts", "file"]) except getopt.GetoptError as err: getopt.usage() sys.exit(2) n = 5 f = "data/contacts.json" for o, a in opts: if o == "-n": n = int(a) elif o == "-f": f = a testdata = [Contact(first_name="", middle_name="", last_name="", nickname="", title="", company="", address="", home_tel="", mobile_tel="", work_tel="", fax="", email="", homepage="", birthday="", anniversary="", secondary_address="", secondary_tel="", notes="")] + [ Contact(first_name=random_string('first_name', 10), middle_name=random_string('middle_name', 10), last_name=random_string('last_name', 10), nickname=random_string('nickname', 10), title=random_string('random_string', 10), company=random_string('company', 10), address=random_string('address', 10), home_tel=random_string('home_tel', 10), mobile_tel=random_string('mobile_tel', 10), work_tel=random_string('work_tel', 10), fax=random_string('fax', 10), email=random_string('email', 10), homepage=random_string('homepage', 10), birthday=random_string('birthday', 10), anniversary=random_string('anniversary', 10), secondary_address=random_string('secondary_address', 10), secondary_tel=random_string('secondary_tel', 10), notes=random_string('notes', 10)) for i in range(5) ] file = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", f) with open(file , "w") as out: jsonpickle.set_encoder_options("json", indent=2) out.write(jsonpickle.encode(testdata))

40.583333

153

0.664271

165cb63df5c2c12565813006cb857ecc7266b584

9,952

py

Python

Lib/test/test_runpy.py

arvindm95/unladen-swallow

8175e37eaea7ca66ed03283b46bc1d2db0d3f9c3

[ "PSF-2.0" ]

2,293

2015-01-02T12:46:10.000Z

2022-03-29T09:45:43.000Z

python/src/Lib/test/test_runpy.py

weiqiangzheng/sl4a

d3c17dca978cbeee545e12ea240a9dbf2a6999e9

[ "Apache-2.0" ]

315

2015-05-31T11:55:46.000Z

2022-01-12T08:36:37.000Z

python/src/Lib/test/test_runpy.py

weiqiangzheng/sl4a

d3c17dca978cbeee545e12ea240a9dbf2a6999e9

[ "Apache-2.0" ]

1,033

2015-01-04T07:48:40.000Z

2022-03-24T09:34:37.000Z

# Test the runpy module import unittest import os import os.path import sys import tempfile from test.test_support import verbose, run_unittest, forget from runpy import _run_code, _run_module_code, run_module # Note: This module can't safely test _run_module_as_main as it # runs its tests in the current process, which would mess with the # real __main__ module (usually test.regrtest) # See test_cmd_line_script for a test that executes that code path # Set up the test code and expected results def test_main(): run_unittest(RunModuleCodeTest) run_unittest(RunModuleTest) if __name__ == "__main__": test_main()

39.181102

82

0.60621

165d5b352de2106b373e88fa207e7c0361117e91

4,795

py

Python

experiments/_pytorch/_grpc_server/protofiles/imagedata_pb2.py

RedisAI/benchmarks

65b8509b81795da73f25f51941c61fbd9765914c

[ "MIT" ]

6

2019-04-18T10:17:52.000Z

2021-07-02T19:57:08.000Z

experiments/_pytorch/_grpc_server/protofiles/imagedata_pb2.py

hhsecond/benchmarks

65b8509b81795da73f25f51941c61fbd9765914c

[ "MIT" ]

1

2021-07-21T12:17:08.000Z

experiments/_pytorch/_grpc_server/protofiles/imagedata_pb2.py

hhsecond/benchmarks

65b8509b81795da73f25f51941c61fbd9765914c

[ "MIT" ]

2

2020-03-15T00:37:57.000Z

2022-02-26T04:36:00.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: imagedata.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='imagedata.proto', package='', syntax='proto3', serialized_options=None, serialized_pb=_b('\n\x0fimagedata.proto\"H\n\tImageData\x12\r\n\x05image\x18\x01 \x01(\x0c\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\r\n\x05\x64type\x18\x04 \x01(\t\"!\n\x0fPredictionClass\x12\x0e\n\x06output\x18\x01 \x03(\x02\x32<\n\tPredictor\x12/\n\rGetPrediction\x12\n.ImageData\x1a\x10.PredictionClass\"\x00\x62\x06proto3') ) _IMAGEDATA = _descriptor.Descriptor( name='ImageData', full_name='ImageData', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='image', full_name='ImageData.image', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='height', full_name='ImageData.height', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='width', full_name='ImageData.width', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='dtype', full_name='ImageData.dtype', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=19, serialized_end=91, ) _PREDICTIONCLASS = _descriptor.Descriptor( name='PredictionClass', full_name='PredictionClass', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='output', full_name='PredictionClass.output', index=0, number=1, type=2, cpp_type=6, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=93, serialized_end=126, ) DESCRIPTOR.message_types_by_name['ImageData'] = _IMAGEDATA DESCRIPTOR.message_types_by_name['PredictionClass'] = _PREDICTIONCLASS _sym_db.RegisterFileDescriptor(DESCRIPTOR) ImageData = _reflection.GeneratedProtocolMessageType('ImageData', (_message.Message,), dict( DESCRIPTOR = _IMAGEDATA, __module__ = 'imagedata_pb2' # @@protoc_insertion_point(class_scope:ImageData) )) _sym_db.RegisterMessage(ImageData) PredictionClass = _reflection.GeneratedProtocolMessageType('PredictionClass', (_message.Message,), dict( DESCRIPTOR = _PREDICTIONCLASS, __module__ = 'imagedata_pb2' # @@protoc_insertion_point(class_scope:PredictionClass) )) _sym_db.RegisterMessage(PredictionClass) _PREDICTOR = _descriptor.ServiceDescriptor( name='Predictor', full_name='Predictor', file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=128, serialized_end=188, methods=[ _descriptor.MethodDescriptor( name='GetPrediction', full_name='Predictor.GetPrediction', index=0, containing_service=None, input_type=_IMAGEDATA, output_type=_PREDICTIONCLASS, serialized_options=None, ), ]) _sym_db.RegisterServiceDescriptor(_PREDICTOR) DESCRIPTOR.services_by_name['Predictor'] = _PREDICTOR # @@protoc_insertion_point(module_scope)

30.935484

365

0.740563

165e5478bb41b24d4a9ab5bce186c085b7367f24

4,937

py

Python

app/api/admin_sales/discounted.py

akashtalole/python-flask-restful-api

475d8fd7be1724183716a197aac4257f8fbbeac4

[ "MIT" ]

3

2019-09-05T05:28:49.000Z

2020-06-10T09:03:37.000Z

app/api/admin_sales/discounted.py

akashtalole/python-flask-restful-api

475d8fd7be1724183716a197aac4257f8fbbeac4

[ "MIT" ]

null

app/api/admin_sales/discounted.py

akashtalole/python-flask-restful-api

475d8fd7be1724183716a197aac4257f8fbbeac4

[ "MIT" ]

null

from sqlalchemy import func from flask_rest_jsonapi import ResourceList from marshmallow_jsonapi import fields from marshmallow_jsonapi.flask import Schema from app.api.helpers.utilities import dasherize from app.api.bootstrap import api from app.models import db from app.models.discount_code import DiscountCode from app.models.event import Event from app.models.order import Order, OrderTicket from app.models.user import User

41.838983

102

0.552157

165e549759c53b8757e058aa4a4e0a0e6b69b060

407

py

Python

spacy/lang/sr/__init__.py

g4brielvs/spaCy

cca8651fc8133172ebaa9d9fc438ed1fbf34fb33

[ "BSD-3-Clause", "MIT" ]

4

2021-08-11T05:46:23.000Z

2021-09-11T05:16:57.000Z

spacy/lang/sr/__init__.py

g4brielvs/spaCy

cca8651fc8133172ebaa9d9fc438ed1fbf34fb33

[ "BSD-3-Clause", "MIT" ]

1

2021-03-01T19:01:37.000Z

spacy/lang/sr/__init__.py

g4brielvs/spaCy

cca8651fc8133172ebaa9d9fc438ed1fbf34fb33

[ "BSD-3-Clause", "MIT" ]

2

2021-01-26T17:29:02.000Z

2021-03-13T08:54:53.000Z

from .stop_words import STOP_WORDS from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS from .lex_attrs import LEX_ATTRS from ...language import Language __all__ = ["Serbian"]

21.421053

54

0.781327

165e63725354de429a448d866f665cccca991916

656

py

Python

mmdet/ops/dcn/__init__.py

TJUsym/TJU_Advanced_CV_Homework

2d85943390e9ba53b80988e0ab8d50aef0cd17da

[ "Apache-2.0" ]

1,158

2019-04-26T01:08:32.000Z

2022-03-30T06:46:24.000Z

mmdet/ops/dcn/__init__.py

TJUsym/TJU_Advanced_CV_Homework

2d85943390e9ba53b80988e0ab8d50aef0cd17da

[ "Apache-2.0" ]

148

2021-03-18T09:44:02.000Z

2022-03-31T06:01:39.000Z

mmdet/ops/dcn/__init__.py

TJUsym/TJU_Advanced_CV_Homework

2d85943390e9ba53b80988e0ab8d50aef0cd17da

[ "Apache-2.0" ]

197

2020-01-29T09:58:27.000Z

2022-03-25T12:08:56.000Z

from .functions.deform_conv import deform_conv, modulated_deform_conv from .functions.deform_pool import deform_roi_pooling from .modules.deform_conv import (DeformConv, ModulatedDeformConv, DeformConvPack, ModulatedDeformConvPack) from .modules.deform_pool import (DeformRoIPooling, DeformRoIPoolingPack, ModulatedDeformRoIPoolingPack) __all__ = [ 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'DeformRoIPooling', 'DeformRoIPoolingPack', 'ModulatedDeformRoIPoolingPack', 'deform_conv', 'modulated_deform_conv', 'deform_roi_pooling' ]

46.857143

76

0.739329

165f2a4da2ed50464bfa13f0495fc689063e0199

1,189

py

Python

api/skill/serializer.py

zaubermaerchen/imas_cg_api

45ebdde8c47ff4fabbf58b75721721f142afb46b

[ "MIT" ]

2

2016-02-01T21:03:53.000Z

2018-10-20T09:15:12.000Z

api/skill/serializer.py

zaubermaerchen/imas_cg_api

45ebdde8c47ff4fabbf58b75721721f142afb46b

[ "MIT" ]

1

2020-01-05T12:50:35.000Z

api/skill/serializer.py

zaubermaerchen/imas_cg_api

45ebdde8c47ff4fabbf58b75721721f142afb46b

[ "MIT" ]

null

# coding: utf-8 from rest_framework import serializers from data.models import Skill, SkillValue

26.422222

72

0.64508

1660d7a15a18998c6c8ae4f9e573b184061a0341

5,061

py

Python

Codes/Converting_RGB_to_GreyScale.py

sichkar-valentyn/Image_processing_in_Python

43d7c979bcd742cc202a28c2dea6ea5bc87562a2

[ "MIT" ]

3

2018-12-02T03:59:51.000Z

2019-11-20T18:37:41.000Z

Codes/Converting_RGB_to_GreyScale.py

sichkar-valentyn/Image_processing_in_Python

43d7c979bcd742cc202a28c2dea6ea5bc87562a2

[ "MIT" ]

null

Codes/Converting_RGB_to_GreyScale.py

sichkar-valentyn/Image_processing_in_Python

43d7c979bcd742cc202a28c2dea6ea5bc87562a2

[ "MIT" ]

2

2018-10-18T07:01:26.000Z

2022-03-22T08:22:33.000Z

# File: Converting_RGB_to_GreyScale.py # Description: Opening RGB image as array, converting to GreyScale and saving result into new file # Environment: PyCharm and Anaconda environment # # MIT License # Copyright (c) 2018 Valentyn N Sichkar # github.com/sichkar-valentyn # # Reference to: # Valentyn N Sichkar. Image processing in Python // GitHub platform. DOI: 10.5281/zenodo.1343603 # Opening RGB image as array, converting to GreyScale and saving result into new file # Importing needed libraries import numpy as np from PIL import Image import matplotlib.pyplot as plt from skimage import color from skimage import io import scipy.misc # Creating an array from image data image_RGB = Image.open("images/eagle.jpg") image_np = np.array(image_RGB) # Checking the type of the array print(type(image_np)) # <class 'numpy.ndarray'> # Checking the shape of the array print(image_np.shape) # Showing image with every channel separately channel_R = image_np[:, :, 0] channel_G = image_np[:, :, 1] channel_B = image_np[:, :, 2] # Creating a figure with subplots f, ax = plt.subplots(nrows=2, ncols=2) # ax is (2, 2) np array and to make it easier to read we use 'flatten' function # Or we can call each time ax[0, 0] ax0, ax1, ax2, ax3 = ax.flatten() # Adjusting first subplot ax0.imshow(channel_R, cmap='Reds') ax0.set_xlabel('') ax0.set_ylabel('') ax0.set_title('Red channel') # Adjusting second subplot ax1.imshow(channel_G, cmap='Greens') ax1.set_xlabel('') ax1.set_ylabel('') ax1.set_title('Green channel') # Adjusting third subplot ax2.imshow(channel_B, cmap='Blues') ax2.set_xlabel('') ax2.set_ylabel('') ax2.set_title('Blue channel') # Adjusting fourth subplot ax3.imshow(image_np) ax3.set_xlabel('') ax3.set_ylabel('') ax3.set_title('Original image') # Function to make distance between figures plt.tight_layout() # Giving the name to the window with figure f.canvas.set_window_title('Eagle image in three channels R, G and B') # Showing the plots plt.show() # Converting RGB image into GrayScale image # Using formula: # Y' = 0.299 R + 0.587 G + 0.114 B image_RGB = Image.open("images/eagle.jpg") image_np = np.array(image_RGB) image_GreyScale = image_np[:, :, 0] * 0.299 + image_np[:, :, 1] * 0.587 + image_np[:, :, 2] * 0.114 # Checking the type of the array print(type(image_GreyScale)) # <class 'numpy.ndarray'> # Checking the shape of the array print(image_GreyScale.shape) # Giving the name to the window with figure plt.figure('GreyScaled image from RGB') # Showing the image by using obtained array plt.imshow(image_GreyScale, cmap='Greys') plt.show() # Preparing array for saving - creating three channels with the same data in each # Firstly, creating array with zero elements # And by 'image_GreyScale.shape + tuple([3])' we add one more element '3' to the tuple # Now the shape will be (1080, 1920, 3) - which is tuple type image_GreyScale_with_3_channels = np.zeros(image_GreyScale.shape + tuple([3])) # Secondly, reshaping GreyScale image from 2D to 3D x = image_GreyScale.reshape((1080, 1920, 1)) # Finally, writing all data in three channels image_GreyScale_with_3_channels[:, :, 0] = x[:, :, 0] image_GreyScale_with_3_channels[:, :, 1] = x[:, :, 0] image_GreyScale_with_3_channels[:, :, 2] = x[:, :, 0] # Saving image into a file from obtained 3D array scipy.misc.imsave("images/result_1.jpg", image_GreyScale_with_3_channels) # Checking that image was written with three channels and they are identical result_1 = Image.open("images/result_1.jpg") result_1_np = np.array(result_1) print(result_1_np.shape) print(np.array_equal(result_1_np[:, :, 0], result_1_np[:, :, 1])) print(np.array_equal(result_1_np[:, :, 1], result_1_np[:, :, 2])) # Showing saved resulted image # Giving the name to the window with figure plt.figure('GreyScaled image from RGB') # Here we don't need to specify the map like cmap='Greys' plt.imshow(result_1_np) plt.show() # Another way to convert RGB image into GreyScale image image_RGB = io.imread("images/eagle.jpg") image_GreyScale = color.rgb2gray(image_RGB) # Checking the type of the array print(type(image_GreyScale)) # <class 'numpy.ndarray'> # Checking the shape of the array print(image_GreyScale.shape) # Giving the name to the window with figure plt.figure('GreyScaled image from RGB') # Showing the image by using obtained array plt.imshow(image_GreyScale, cmap='Greys') plt.show() # Saving converted image into a file from processed array scipy.misc.imsave("images/result_2.jpg", image_GreyScale) # One more way for converting image_RGB_as_GreyScale = io.imread("images/eagle.jpg", as_gray=True) # Checking the type of the array print(type(image_RGB_as_GreyScale)) # <class 'numpy.ndarray'> # Checking the shape of the array print(image_RGB_as_GreyScale.shape) # Giving the name to the window with figure plt.figure('GreyScaled image from RGB') # Showing the image by using obtained array plt.imshow(image_RGB_as_GreyScale, cmap='Greys') plt.show() # Saving converted image into a file from processed array scipy.misc.imsave("images/result_3.jpg", image_RGB_as_GreyScale)

33.966443

99

0.752223

1661f7c0c438355d7d875aa2c983973094881c84

3,193

py

Python

template_renderer.py

hamza-gheggad/gcp-iam-collector

02b46453b9ec23af07a0d81f7250f1de61e0ee23

[ "Apache-2.0" ]

null

template_renderer.py

hamza-gheggad/gcp-iam-collector

02b46453b9ec23af07a0d81f7250f1de61e0ee23

[ "Apache-2.0" ]

null

template_renderer.py

hamza-gheggad/gcp-iam-collector

02b46453b9ec23af07a0d81f7250f1de61e0ee23

[ "Apache-2.0" ]

null

import colorsys import json from jinja2 import Environment, PackageLoader import graph

31.303922

91

0.593173

166293ba707b563d24827825716e3e79a6848c40

13,007

py

Python

powerapi/cli/tools.py

danglotb/powerapi

67b2508588bfe1e20d90f9fe6bccda34d3455262

[ "BSD-3-Clause" ]

null

powerapi/cli/tools.py

danglotb/powerapi

67b2508588bfe1e20d90f9fe6bccda34d3455262

[ "BSD-3-Clause" ]

null

powerapi/cli/tools.py

danglotb/powerapi

67b2508588bfe1e20d90f9fe6bccda34d3455262

[ "BSD-3-Clause" ]

null

# Copyright (c) 2018, INRIA # Copyright (c) 2018, University of Lille # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import os import sys import logging from functools import reduce from powerapi.exception import PowerAPIException from powerapi.cli.parser import MainParser, ComponentSubParser from powerapi.cli.parser import store_true from powerapi.cli.parser import BadValueException, MissingValueException from powerapi.cli.parser import BadTypeException, BadContextException from powerapi.cli.parser import UnknowArgException from powerapi.report_model import HWPCModel, PowerModel, FormulaModel, ControlModel from powerapi.database import MongoDB, CsvDB, InfluxDB, OpenTSDB from powerapi.puller import PullerActor from powerapi.pusher import PusherActor

49.268939

128

0.667948

1662a331dbe1e237d08e9e21a3e8d596bcbce6c4

2,477

py

Python

pyxrd/mixture/models/insitu_behaviours/insitu_behaviour.py

PyXRD/pyxrd

26bacdf64f3153fa74b8caa62e219b76d91a55c1

[ "BSD-2-Clause" ]

27

2018-06-15T15:28:18.000Z

2022-03-10T12:23:50.000Z

pyxrd/mixture/models/insitu_behaviours/insitu_behaviour.py

PyXRD/pyxrd

26bacdf64f3153fa74b8caa62e219b76d91a55c1

[ "BSD-2-Clause" ]

22

2018-06-14T08:29:16.000Z

2021-07-05T13:33:44.000Z

pyxrd/mixture/models/insitu_behaviours/insitu_behaviour.py

PyXRD/pyxrd

26bacdf64f3153fa74b8caa62e219b76d91a55c1

[ "BSD-2-Clause" ]

8

2019-04-13T13:03:51.000Z

2021-06-19T09:29:11.000Z

# coding=UTF-8 # ex:ts=4:sw=4:et=on # # Copyright (c) 2013, Mathijs Dumon # All rights reserved. # Complete license can be found in the LICENSE file. from mvc.models.properties import StringProperty from pyxrd.generic.io.custom_io import storables, Storable from pyxrd.generic.models.base import DataModel from pyxrd.refinement.refinables.mixins import RefinementGroup

34.402778

103

0.583771

16635cf724808862aeb33d75c907fed77d96d1fc

857

py

Python

1 plainProgrammingBug/start 1 plainProgrammingBug.py

vishalbelsare/SLAPP3

da187b771831aaaabaee16a26ad341db2e968104

[ "CC0-1.0" ]

8

2017-10-18T05:19:17.000Z

2020-03-24T21:23:52.000Z

1 plainProgrammingBug/start 1 plainProgrammingBug.py

vishalbelsare/SLAPP3

da187b771831aaaabaee16a26ad341db2e968104

[ "CC0-1.0" ]

null

1 plainProgrammingBug/start 1 plainProgrammingBug.py

vishalbelsare/SLAPP3

da187b771831aaaabaee16a26ad341db2e968104

[ "CC0-1.0" ]

4

2017-10-25T09:07:49.000Z

2019-08-18T09:17:58.000Z

# start 1 plainProgrammingBug.py import random # returns -1, 0, 1 with equal probability SimpleBug() """ you can eliminate the randomMove() function substituting xPos += randomMove() yPos += randomMove() with xPos += random.randint(-1, 1) yPos += random.randint(-1, 1) but the use of the function allows us to use here a self-explanatory name """

19.930233

69

0.568261

166407e573ed13b6f495ddb118b6bb572fdf1148

423

py

Python

ba5a-min-coins/money_change.py

kjco/bioinformatics-algorithms

3c466157b89c1cbd54749563e39d86a307d7a3f3

[ "MIT" ]

null

ba5a-min-coins/money_change.py

kjco/bioinformatics-algorithms

3c466157b89c1cbd54749563e39d86a307d7a3f3

[ "MIT" ]

null

ba5a-min-coins/money_change.py

kjco/bioinformatics-algorithms

3c466157b89c1cbd54749563e39d86a307d7a3f3

[ "MIT" ]

null

money = 8074 #money = 18705 #coin_list = [24,23,21,5,3,1] coin_list = [24,13,12,7,5,3,1] #coin_list = map(int, open('dataset_71_8.txt').read().split(',')) d = {0:0} for m in range(1,money+1): min_coin = 1000000 for coin in coin_list: if m >= coin: if d[m-coin]+1 < min_coin: min_coin = d[m-coin]+1 d[m] = min_coin #print d print d[money]

18.391304

66

0.51773

1665579643c424a545b6a8b3af94a1a9e0f4f184

357

py

Python

examples/remove_comments.py

igordejanovic/textx-bibtex

b1374a39b96da9c1bc979c367b9ed3feb04f4f01

[ "MIT" ]

1

2020-06-17T21:51:33.000Z

examples/remove_comments.py

igordejanovic/textx-bibtex

b1374a39b96da9c1bc979c367b9ed3feb04f4f01

[ "MIT" ]

null

examples/remove_comments.py

igordejanovic/textx-bibtex

b1374a39b96da9c1bc979c367b9ed3feb04f4f01

[ "MIT" ]

null

""" Remove comments from bib file. """ from textx import metamodel_for_language from txbibtex import bibentry_str BIB_FILE = 'references.bib' bibfile = metamodel_for_language('bibtex').model_from_file(BIB_FILE) # Drop line comments. print('\n'.join([bibentry_str(e) for e in bibfile.entries if e.__class__.__name__ != 'BibLineComment']))

27.461538

68

0.739496

1665f41d1c03f32167e2cea236d3cf7a022b6b61

3,202

py

Python

google-cloud-sdk/lib/surface/compute/resource_policies/create/group_placement.py

bopopescu/Social-Lite

ee05d6a7431c36ff582c8d6b58bb20a8c5f550bf

[ "Apache-2.0" ]

null

google-cloud-sdk/lib/surface/compute/resource_policies/create/group_placement.py

bopopescu/Social-Lite

ee05d6a7431c36ff582c8d6b58bb20a8c5f550bf

[ "Apache-2.0" ]

4

2020-07-21T12:51:46.000Z

2022-01-22T10:29:25.000Z

google-cloud-sdk/lib/surface/compute/resource_policies/create/group_placement.py

bopopescu/Social-Lite

ee05d6a7431c36ff582c8d6b58bb20a8c5f550bf

[ "Apache-2.0" ]

1

2020-07-25T18:17:57.000Z

# -*- coding: utf-8 -*- # # Copyright 2019 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Create resource policy command.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.api_lib.compute import base_classes from googlecloudsdk.api_lib.compute import utils as compute_api from googlecloudsdk.api_lib.util import apis from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.compute import flags as compute_flags from googlecloudsdk.command_lib.compute.resource_policies import flags from googlecloudsdk.command_lib.compute.resource_policies import util def _CommonArgs(parser, api_version): """A helper function to build args based on different API version.""" messages = apis.GetMessagesModule('compute', api_version) flags.MakeResourcePolicyArg().AddArgument(parser) flags.AddCommonArgs(parser) flags.AddGroupPlacementArgs(parser, messages) parser.display_info.AddCacheUpdater(None) CreateGroupPlacement.detailed_help = { 'DESCRIPTION': """\ Create a Google Compute Engine Group Placement Resource Policy. """, 'EXAMPLES': """\ To create a Google Compute Engine Group Placement Resource policy with 2 VMs and 2 availability domains, run: $ {command} my-resource-policy --region=REGION --vm-count=2 --availability-domain-count=2 """ }

37.232558

109

0.777327

16661518293e1bbad26be3766a9addb9bc564758

629

py

Python

paperoni/io.py

notoraptor/paperoni

acdf2d3d790b98d6a171177ffd9d6342f86bc7ea

[ "MIT" ]

88

2020-08-27T17:58:58.000Z

2021-12-01T19:29:56.000Z

paperoni/io.py

notoraptor/paperoni

acdf2d3d790b98d6a171177ffd9d6342f86bc7ea

[ "MIT" ]

8

2020-08-27T02:54:11.000Z

2022-02-01T13:35:41.000Z

paperoni/io.py

notoraptor/paperoni

acdf2d3d790b98d6a171177ffd9d6342f86bc7ea

[ "MIT" ]

6

2020-08-25T16:43:28.000Z

2021-12-08T16:41:02.000Z

import json from .papers import Papers from .researchers import Researchers def ResearchersFile(filename): """Parse a file containing researchers.""" try: with open(filename, "r") as file: data = json.load(file) except FileNotFoundError: data = {} return Researchers(data, filename=filename) def PapersFile(filename, researchers=None): """Parse a file containing papers.""" try: with open(filename, "r") as file: data = json.load(file) except FileNotFoundError: data = {} return Papers(data, filename=filename, researchers=researchers)

25.16

67

0.655008

16666943ca1f78d9acd45c2909883bd0b65b734d

934

py

Python

src/lib/sd2/test_addresses.py

zachkont/sd2

92d8c55a8c7ac51c00ba514be01955aa7162e4ef

[ "Apache-2.0" ]

null

src/lib/sd2/test_addresses.py

zachkont/sd2

92d8c55a8c7ac51c00ba514be01955aa7162e4ef

[ "Apache-2.0" ]

null

src/lib/sd2/test_addresses.py

zachkont/sd2

92d8c55a8c7ac51c00ba514be01955aa7162e4ef

[ "Apache-2.0" ]

null

############################################################################# # Copyright (c) 2017 SiteWare Corp. All right reserved ############################################################################# import logging import pytest from . import addresses

30.129032

77

0.626338

166739b28ed7ffa22c5f71499709f1fd302bd933

1,914

py

Python

config_model.py

Asha-ai/BERT_abstractive_proj

f0e8f659d6b8821cfe0d15f4075e8cb890efdfe9

[ "Apache-2.0" ]

17

2020-01-11T15:15:21.000Z

2021-12-08T10:03:36.000Z

config_model.py

Asha-ai/BERT_abstractive_proj

f0e8f659d6b8821cfe0d15f4075e8cb890efdfe9

[ "Apache-2.0" ]

6

2020-03-01T17:14:58.000Z

2021-05-21T16:05:03.000Z

config_model.py

Asha-ai/BERT_abstractive_proj

f0e8f659d6b8821cfe0d15f4075e8cb890efdfe9

[ "Apache-2.0" ]

8

2020-05-11T21:24:51.000Z

2021-07-23T09:18:46.000Z

import texar.tf as tx beam_width = 5 hidden_dim = 768 bert = { 'pretrained_model_name': 'bert-base-uncased' } # See https://texar.readthedocs.io/en/latest/code/modules.html#texar.tf.modules.BERTEncoder.default_hparams bert_encoder = {} # From https://github.com/asyml/texar/blob/413e07f859acbbee979f274b52942edd57b335c1/examples/transformer/config_model.py#L27-L45 # with adjustments for BERT decoder = { 'dim': hidden_dim, 'num_blocks': 6, 'multihead_attention': { 'num_heads': 8, 'output_dim': hidden_dim }, 'initializer': { 'type': 'variance_scaling_initializer', 'kwargs': { 'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', }, }, 'poswise_feedforward': tx.modules.default_transformer_poswise_net_hparams(output_dim=hidden_dim) } loss_label_confidence = 0.9 opt = { 'optimizer': { 'type': 'AdamOptimizer', 'kwargs': { 'beta1': 0.9, 'beta2': 0.997, 'epsilon': 1e-9 } } } lr = { # The 'learning_rate_schedule' can have the following 3 values: # - 'static' -> A simple static learning rate, specified by 'static_lr' # - 'aiayn' -> The learning rate used in the "Attention is all you need" paper. # - 'constant.linear_warmup.rsqrt_decay.rsqrt_depth' -> The learning rate for Texar's Transformer example 'learning_rate_schedule': 'aiayn', # The learning rate constant used for the 'constant.linear_warmup.rsqrt_decay.rsqrt_depth' learning rate 'lr_constant': 2 * (hidden_dim ** -0.5), # The warmup steps for the 'aiayn' and 'constant.linear_warmup.rsqrt_decay.rsqrt_depth' learning rate 'warmup_steps': 4000, # The static learning rate, when 'static' is used. 'static_lr': 1e-3, # A multiplier that can be applied to the 'aiayn' learning rate. 'aiayn_multiplier': 0.2 }

31.377049

128

0.653083

16677a6fe2ff1b1e4b01bda4446f100594d88c8e

390

py

Python

wishes/migrations/0005_auto_20201029_0904.py

e-elson/bd

e35c59686e5ec81925c22353e269601f286634db

[ "MIT" ]

null

wishes/migrations/0005_auto_20201029_0904.py

e-elson/bd

e35c59686e5ec81925c22353e269601f286634db

[ "MIT" ]

null

wishes/migrations/0005_auto_20201029_0904.py

e-elson/bd

e35c59686e5ec81925c22353e269601f286634db

[ "MIT" ]

null

# Generated by Django 3.1.2 on 2020-10-29 09:04 from django.db import migrations, models

20.526316

55

0.594872

166802c5b61892041a13896dbed6ef514fd83df2

7,115

py

Python

undeployed/legacy/Landsat/DNtoReflectance.py

NASA-DEVELOP/dnppy

8f7ef6f0653f5a4ea730ee557c72a2c89c06ce0b

[ "NASA-1.3" ]

65

2015-09-10T12:59:56.000Z

2022-02-27T22:09:03.000Z

undeployed/legacy/Landsat/DNtoReflectance.py

snowzm/dnppy

8f7ef6f0653f5a4ea730ee557c72a2c89c06ce0b

[ "NASA-1.3" ]

40

2015-04-08T19:23:30.000Z

2015-08-04T15:53:11.000Z

undeployed/legacy/Landsat/DNtoReflectance.py

snowzm/dnppy

8f7ef6f0653f5a4ea730ee557c72a2c89c06ce0b

[ "NASA-1.3" ]

45

2015-08-14T19:09:38.000Z

2022-02-15T18:53:16.000Z

#------------------------------------------------------------------------------- # Name: Landsat Digital Numbers to Radiance/Reflectance # Purpose: To convert landsat 4,5, or 7 pixel values from digital numbers # to Radiance, Reflectance, or Temperature # Author: Quinten Geddes [email protected] # NASA DEVELOP Program # Created: 19/10/2012 #------------------------------------------------------------------------------- import arcpy import math arcpy.CheckOutExtension("Spatial") def DNtoReflectance(Lbands,MetaData,OutputType="Reflectance/Temperature",Save=False,OutputFolder=""): """This function is used to convert Landsat 4,5, or 7 pixel values from digital numbers to Radiance, Reflectance, or Temperature (if using Band 6) -----Inputs------ Lbands: GeoTIFF files containing individual bands of Landsat imagery. These must have the original names as downloaded and must be from a single scene. MetaData: The metadata text file that is downloaded with the Landsat Bands themselves. This may be either the old or new MTL.txt file. OutputType: Choose whether the output should be: "Radiance" "Reflectance/Temperature" - Calculates Reflectance for spectral bands and Temperature in Kelvin for Thermal bands Save: Boolean value that indicates whether the output rasters will be saved permanantly Each band will be saved as an individual GeoTIFF file and be named accoriding to the original filename and the output pixel unit *if this is true, then the OutputFolder variable must also be set OutputFolder: Folder in which to save the output rasters -----Outputs----- A list of arcpy raster objects in a sequence that mirrors that of the input Lbands """ OutList=[] #These lists will be used to parse the meta data text file and locate relevant information #metadata format was changed August 29, 2012. This tool can process either the new or old format newMeta=['LANDSAT_SCENE_ID = "','DATE_ACQUIRED = ',"SUN_ELEVATION = ", "RADIANCE_MAXIMUM_BAND_{0} = ","RADIANCE_MINIMUM_BAND_{0} = ", "QUANTIZE_CAL_MAX_BAND_{0} = ","QUANTIZE_CAL_MIN_BAND_{0} = "] oldMeta=['BAND1_FILE_NAME = "',"ACQUISITION_DATE = ","SUN_ELEVATION = ", "LMAX_BAND{0} = ","LMIN_BAND{0} = ", "QCALMAX_BAND{0} = ","QCALMIN_BAND{0} = "] f=open(MetaData) MText=f.read() #the presence of a PRODUCT_CREATION_TIME category is used to identify old metadata #if this is not present, the meta data is considered new. #Band6length refers to the length of the Band 6 name string. In the new metadata this string is longer if "PRODUCT_CREATION_TIME" in MText: Meta=oldMeta Band6length=2 else: Meta=newMeta Band6length=8 #The tilename is located using the newMeta/oldMeta indixes and the date of capture is recorded if Meta==newMeta: TileName=MText.split(Meta[0])[1].split('"')[0] year=TileName[9:13] jday=TileName[13:16] elif Meta==oldMeta: TileName=MText.split(Meta[0])[1].split('"')[0] year=TileName[13:17] jday=TileName[17:20] date=MText.split(Meta[1])[1].split('\n')[0] #the spacecraft from which the imagery was capture is identified #this info determines the solar exoatmospheric irradiance (ESun) for each band spacecraft=MText.split('SPACECRAFT_ID = "')[1].split('"')[0] ThermBands=["6"] if "7" in spacecraft: ESun=(1969.0,1840.0,1551.0,1044.0,255.700,0. ,82.07,1368.00) ThermBands=["B6_VCID_1","B6_VCID_2"] elif "5" in spacecraft: ESun=(1957.0,1826.0,1554.0,1036.0,215.0 ,0. ,80.67) elif "4" in spacecraft: ESun=(1957.0,1825.0,1557.0,1033.0,214.9 ,0. ,80.72) elif "8" in spacecraft: ESun=(1857.0,1996.0,1812.0,1516.0,983.3 ,251.8,85.24,0.0,389.3,0.,0.) ThermBands=["10","11"] else: arcpy.AddError("This tool only works for Landsat 4, 5, 7 or 8 ") raise arcpy.ExecuteError() #determing if year is leap year and setting the Days in year accordingly if float(year) % 4 ==0: DIY=366. else:DIY=365. #using the date to determing the distance from the sun theta =2*math.pi*float(jday)/DIY dSun2 = (1.00011 + 0.034221*math.cos(theta) + 0.001280*math.sin(theta) + 0.000719*math.cos(2*theta)+ 0.000077*math.sin(2*theta) ) SZA=90.-float(MText.split(Meta[2])[1].split("\n")[0]) #Calculating values for each band for pathname in Lbands: try: BandNum=pathname.split("\\")[-1].split("B")[1][0:2] try: int(BandNum) except: BandNum=pathname.split("\\")[-1].split("B")[1][0] except: msg="Error reading Band {0}. Bands must have original names as downloaded.".format(str(inputbandnum)) arcpy.AddError(msg) print msg raise arcpy.ExecuteError #changing Band 6 name to match metadata if BandNum=="6" and spacecraft[8]=="7": BandNum=pathname.split("\\")[-1].split("B")[1][0:Band6length] print "Processing Band {0}".format(BandNum) Oraster=arcpy.Raster(pathname) #using the oldMeta/newMeta indixes to pull the min/max for radiance/Digital numbers LMax= float(MText.split(Meta[3].format(BandNum))[1].split("\n")[0]) LMin= float(MText.split(Meta[4].format(BandNum))[1].split("\n")[0]) QCalMax=float(MText.split(Meta[5].format(BandNum))[1].split("\n")[0]) QCalMin=float(MText.split(Meta[6].format(BandNum))[1].split("\n")[0]) Radraster=(((LMax - LMin)/(QCalMax-QCalMin)) * (Oraster - QCalMin)) +LMin Oraster=0 if OutputType=="Radiance": Radraster.save("{0}\\{1}_B{2}_Radiance.tif".format(OutputFolder,TileName,BandNum)) Radraster=0 elif OutputType=="Reflectance/Temperature": #Calculating temperature for band 6 if present if BandNum in ThermBands: Refraster=1282.71/(arcpy.sa.Ln((666.09/Radraster)+1.0)) BandPath="{0}\\{1}_B{2}_Temperature.tif".format(OutputFolder,TileName,BandNum) arcpy.AddMessage("Proceeded through if") #Otherwise calculate reflectance else: Refraster=( math.pi * Radraster * dSun2) / (ESun[int(BandNum[0])-1] * math.cos(SZA*math.pi/180) ) BandPath="{0}\\{1}_B{2}_TOA_Reflectance.tif".format(OutputFolder,TileName,BandNum) arcpy.AddMessage("Proceeded through else") if Save==True: Refraster.save(BandPath) OutList.append(arcpy.Raster(BandPath)) else: OutList.append(Refraster) del Refraster,Radraster arcpy.AddMessage( "Reflectance Calculated for Band {0}".format(BandNum)) print "Reflectance Calculated for Band {0}".format(BandNum) f.close() return OutList

42.100592

113

0.619115

1668b92419e5394d4eb735fba074c84b5eb16b19

1,396

py

Python

.modules/.theHarvester/discovery/twittersearch.py

termux-one/EasY_HaCk

0a8d09ca4b126b027b6842e02fa0c29d8250e090

[ "Apache-2.0" ]

1,103

2018-04-20T14:08:11.000Z

2022-03-29T06:22:43.000Z

.modules/.theHarvester/discovery/twittersearch.py

sshourya948/EasY_HaCk

0a8d09ca4b126b027b6842e02fa0c29d8250e090

[ "Apache-2.0" ]

29

2019-04-03T14:52:38.000Z

2022-03-24T12:33:05.000Z

.modules/.theHarvester/discovery/twittersearch.py

sshourya948/EasY_HaCk

0a8d09ca4b126b027b6842e02fa0c29d8250e090

[ "Apache-2.0" ]

262

2017-09-16T22:15:50.000Z

2022-03-31T00:38:42.000Z

import string import requests import sys import myparser import re

32.465116

169

0.592407

166903b8515452d27e1a1b1b4a84d3d174d4f220

708

py

Python

scrap_instagram.py

genaforvena/nn_scrapper

897766a52202aa056afd657995ed39b2b91e1fe2

[ "Apache-2.0" ]

null

scrap_instagram.py

genaforvena/nn_scrapper

897766a52202aa056afd657995ed39b2b91e1fe2

[ "Apache-2.0" ]

null

scrap_instagram.py

genaforvena/nn_scrapper

897766a52202aa056afd657995ed39b2b91e1fe2

[ "Apache-2.0" ]

null

import urllib.request import json access_token = "265791501.a4af066.f45a9f44719a4b2cb2d137118524e32b" api_url = "https://api.instagram.com/v1" nn_lat = 56.296504 nn_lng = 43.936059 locations = request("/locations/search", "lat=" + str(nn_lat) + "&lng=" + str(nn_lng))["data"] print(locations) for location in locations: location_id = location["id"] location_media = request("/locations/" + str(location_id) + "/media/recent") print(location_media)

29.5

94

0.706215

16693286bda8fc5cb36e02f9aa7765ff20fcfe4e

7,066

py

Python

tests/unit/utils/test_validators.py

kajusK/HiddenPlaces

aa976f611a419bc33f8a65f0314956ec09fe2bfd

[ "MIT" ]

null

tests/unit/utils/test_validators.py

kajusK/HiddenPlaces

aa976f611a419bc33f8a65f0314956ec09fe2bfd

[ "MIT" ]

null

tests/unit/utils/test_validators.py

kajusK/HiddenPlaces

aa976f611a419bc33f8a65f0314956ec09fe2bfd

[ "MIT" ]

null

"""Unit tests for app.validators. """ from wtforms import ValidationError import flask from pytest import raises from app.utils.validators import password_rules, image_file, allowed_file def _run_validator_check(subtests, validator, valid, invalid): """Runs tests again validator with valid and invalid inputs. Args: subtest: Subtests fixture. validator: Validator instance to run tests against valid: List of valid inputs invalid: List of invalid inputs """ field = DummyField() for item in valid: field.data = item with subtests.test(item=item): validator(DummyForm(), field) for item in invalid: field.data = item with subtests.test(item=item): with raises(ValidationError): validator(DummyForm(), field)

35.686869

78

0.644495

166add4d1cc09be73d6135b394a15f57ecfca1b9

615

py

Python

ts_eval/utils/nans.py

vshulyak/ts-eval

2049b1268cf4272f5fa1471851523f8da14dd84c

[ "MIT" ]

1

2021-07-12T08:58:07.000Z

ts_eval/utils/nans.py

vshulyak/ts-eval

2049b1268cf4272f5fa1471851523f8da14dd84c

[ "MIT" ]

null

ts_eval/utils/nans.py

vshulyak/ts-eval

2049b1268cf4272f5fa1471851523f8da14dd84c

[ "MIT" ]

null

import warnings import numpy as np def nans_in_same_positions(*arrays): """ Compares all provided arrays to see if they have NaNs in the same positions. """ if len(arrays) == 0: return True for arr in arrays[1:]: if not (np.isnan(arrays[0]) == np.isnan(arr)).all(): return False return True def nanmeanw(arr, axis=None): """ Computes nanmean without raising a warning in case of NaNs in the dataset """ with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) return np.nanmean(arr, axis=axis)

24.6

80

0.642276

166b671e9115e476c69bab6e6077599dd6b6cdea

5,434

py

Python

tests/authorization/test_searches.py

UOC/dlkit

a9d265db67e81b9e0f405457464e762e2c03f769

[ "MIT" ]

2

2018-02-23T12:16:11.000Z

2020-10-08T17:54:24.000Z

tests/authorization/test_searches.py

UOC/dlkit

a9d265db67e81b9e0f405457464e762e2c03f769

[ "MIT" ]

87

2017-04-21T18:57:15.000Z

2021-12-13T19:43:57.000Z

tests/authorization/test_searches.py

UOC/dlkit

a9d265db67e81b9e0f405457464e762e2c03f769

[ "MIT" ]

1

2018-03-01T16:44:25.000Z

"""Unit tests of authorization searches.""" import pytest from ..utilities.general import is_never_authz, is_no_authz, uses_cataloging, uses_filesystem_only from dlkit.abstract_osid.osid import errors from dlkit.primordium.id.primitives import Id from dlkit.primordium.type.primitives import Type from dlkit.runtime import PROXY_SESSION, proxy_example from dlkit.runtime.managers import Runtime REQUEST = proxy_example.SimpleRequest() CONDITION = PROXY_SESSION.get_proxy_condition() CONDITION.set_http_request(REQUEST) PROXY = PROXY_SESSION.get_proxy(CONDITION) DEFAULT_TYPE = Type(**{'identifier': 'DEFAULT', 'namespace': 'DEFAULT', 'authority': 'DEFAULT'})

36.469799

176

0.749724

166ccaa355ece2f923c461999fa3eb16171b7163

350

py

Python

mechroutines/models/_flux.py

keceli/mechdriver

978994ba5c77b6df00078b639c4482dacf269440

[ "Apache-2.0" ]

1

2022-03-22T20:47:04.000Z

mechroutines/models/_flux.py

keceli/mechdriver

978994ba5c77b6df00078b639c4482dacf269440

[ "Apache-2.0" ]

1

2021-02-12T21:11:16.000Z

2021-12-07T21:32:14.000Z

mechroutines/models/_flux.py

keceli/mechdriver

978994ba5c77b6df00078b639c4482dacf269440

[ "Apache-2.0" ]

8

2019-12-18T20:09:46.000Z

2020-11-14T16:37:28.000Z

""" NEW: Handle flux files """ import autofile def read_flux(ts_save_path, vrc_locs=(0,)): """ Read the geometry from the filesys """ vrc_fs = autofile.fs.vrctst(ts_save_path) if vrc_fs[-1].file.flux.exists(vrc_locs): flux_str = vrc_fs[-1].file.flux.read(vrc_locs) else: flux_str = None return flux_str

18.421053

54

0.64

166ddfdb964d4dc41f4f840af0cda8cfbfe5a687

4,990

py

Python

RandomForest/RandomForest.py

nachiket273/ML_Algo_Implemented

74ae47fdf620545fdf8c934c5997784faadaebb7

[ "MIT" ]

7

2020-08-03T13:43:53.000Z

2022-02-18T20:38:51.000Z

RandomForest/RandomForest.py

nachiket273/ML_Algo_Implemented

74ae47fdf620545fdf8c934c5997784faadaebb7

[ "MIT" ]

null

RandomForest/RandomForest.py

nachiket273/ML_Algo_Implemented

74ae47fdf620545fdf8c934c5997784faadaebb7

[ "MIT" ]

2

2020-09-06T21:54:16.000Z

2022-01-22T19:59:33.000Z

import math import numpy as np import pandas as pd from sklearn.base import BaseEstimator import sys import os sys.path.append(os.path.abspath('../DecisionTree')) from DecisionTree import DecisionTree

40.901639

93

0.546293

166e1671aebcb4e327d8e4f8b8b62dc58ec16062

556

py

Python

tests/basics/generator_pend_throw.py

iotctl/pycopy

eeb841afea61b19800d054b3b289729665fc9aa4

[ "MIT" ]

663

2018-12-30T00:17:59.000Z

2022-03-14T05:03:41.000Z

tests/basics/generator_pend_throw.py

iotctl/pycopy

eeb841afea61b19800d054b3b289729665fc9aa4

[ "MIT" ]

41

2019-06-06T08:31:19.000Z

2022-02-13T16:53:41.000Z

tests/basics/generator_pend_throw.py

iotctl/pycopy

eeb841afea61b19800d054b3b289729665fc9aa4

[ "MIT" ]

60

2019-06-01T04:25:00.000Z

2022-02-25T01:47:31.000Z

g = gen() try: g.pend_throw except AttributeError: print("SKIP") raise SystemExit print(next(g)) print(next(g)) g.pend_throw(ValueError()) v = None try: v = next(g) except Exception as e: print("raised", repr(e)) print("ret was:", v) # It's legal to pend exception in a just-started generator, just the same # as it's legal to .throw() into it. g = gen() g.pend_throw(ValueError()) try: next(g) except ValueError: print("ValueError from just-started gen")

15.444444

73

0.624101

166e4003ce5bc54874ebae493377303b4c270f29

4,511

py

Python

src/UnitTypes/ProjectileModule.py

USArmyResearchLab/ARL_Battlespace

2f17a478f62c20a4db387d5d3e4bbeaa3197cd49

[ "MIT" ]

1

2022-03-31T19:15:04.000Z

src/UnitTypes/ProjectileModule.py

USArmyResearchLab/ARL_Battlespace

2f17a478f62c20a4db387d5d3e4bbeaa3197cd49

[ "MIT" ]

null

src/UnitTypes/ProjectileModule.py

USArmyResearchLab/ARL_Battlespace

2f17a478f62c20a4db387d5d3e4bbeaa3197cd49

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Dec 15 09:49:47 2020 @author: james.z.hare """ from src.UnitModule import UnitClass, advance from copy import deepcopy import math # Will be used as the projectile for the missile launcher unit

32.221429

158

0.62137

166ed868a00e2876de6024b3dcf661e7d6afc455

216

py

Python

OOP_MiniQuiz/run_car_Level2.py

HelloYeew/helloyeew-lab-computer-programming-i

60b05072f32f23bab4a336b506ba7f66e52c045d

[ "MIT" ]

null

OOP_MiniQuiz/run_car_Level2.py

HelloYeew/helloyeew-lab-computer-programming-i

60b05072f32f23bab4a336b506ba7f66e52c045d

[ "MIT" ]

null

OOP_MiniQuiz/run_car_Level2.py

HelloYeew/helloyeew-lab-computer-programming-i

60b05072f32f23bab4a336b506ba7f66e52c045d

[ "MIT" ]

null

from car import * car1 = Car("Nissan","Tiida",450000) car2 = Car("Toyota","Vios",400000) car3 = Car("BMW","X3",3400000) compare(car3,car1) compare(car1,car2)

18

35

0.671296

166f10041a007d09adb3797f8fd4bf54942b5eeb

1,513

py

Python

prelude/monads.py

michel-slm/python-prelude

b3ca89ff2bf150f772764f59d2796d2fcce1013d

[ "MIT" ]

2

2015-05-12T16:12:56.000Z

2020-08-26T20:52:47.000Z

prelude/monads.py

michel-slm/python-prelude

b3ca89ff2bf150f772764f59d2796d2fcce1013d

[ "MIT" ]

null

prelude/monads.py

michel-slm/python-prelude

b3ca89ff2bf150f772764f59d2796d2fcce1013d

[ "MIT" ]

null

from abc import ABCMeta, abstractmethod from prelude.typeclasses import Monad from prelude.decorators import monad_eq, singleton

18.9125

50

0.613351

16715a2b77e2526acf8bf40591ec7bc531389bde

848

py

Python

Deep Sort/src/imgconverter.py

JJavier98/TFG-Dron-de-Vigilancia

7fd68a981854ac480ad2f0c936a0dd58d2a9f38b

[ "MIT" ]

null

Deep Sort/src/imgconverter.py

JJavier98/TFG-Dron-de-Vigilancia

7fd68a981854ac480ad2f0c936a0dd58d2a9f38b

[ "MIT" ]

null

Deep Sort/src/imgconverter.py

JJavier98/TFG-Dron-de-Vigilancia

7fd68a981854ac480ad2f0c936a0dd58d2a9f38b

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import print_function import roslib roslib.load_manifest('msgs_to_cv2') import sys import rospy import cv2 from std_msgs.msg import String from sensor_msgs.msg import Image from cv_bridge import CvBridge, CvBridgeError if __name__ == '__main__': main(sys.argv)

20.190476

77

0.741745

16718d7813439bbbc33bc80e98b6e4741d2b5b6c

261

py

Python

foodx_devops_tools/azure/__init__.py

Food-X-Technologies/foodx_devops_tools

57d1bf1304d9c9a386eaffa427f9eb36c410c350

[ "MIT" ]

3

2021-06-23T20:53:43.000Z

2022-01-26T14:19:43.000Z

foodx_devops_tools/azure/__init__.py

Food-X-Technologies/foodx_devops_tools

57d1bf1304d9c9a386eaffa427f9eb36c410c350

[ "MIT" ]

33

2021-08-09T15:44:51.000Z

2022-03-03T18:28:02.000Z

foodx_devops_tools/azure/__init__.py

Food-X-Technologies/foodx_devops_tools

57d1bf1304d9c9a386eaffa427f9eb36c410c350

[ "MIT" ]

1

2021-06-23T20:53:52.000Z

# Copyright (c) 2021 Food-X Technologies # # This file is part of foodx_devops_tools. # # You should have received a copy of the MIT License along with # foodx_devops_tools. If not, see <https://opensource.org/licenses/MIT>. """Azure related utilities."""

29

73

0.731801

16725a52de27142aa18864c727dddea44204b666

5,940

py

Python

beartype/vale/__init__.py

posita/beartype

e56399686e1f2ffd5128a4030b19314504e32450

[ "MIT" ]

null

beartype/vale/__init__.py

posita/beartype

e56399686e1f2ffd5128a4030b19314504e32450

[ "MIT" ]

null

beartype/vale/__init__.py

posita/beartype

e56399686e1f2ffd5128a4030b19314504e32450

[ "MIT" ]

null

#!/usr/bin/env python3 # --------------------( LICENSE )-------------------- # Copyright (c) 2014-2021 Beartype authors. # See "LICENSE" for further details. ''' **Beartype validators.** This submodule publishes a PEP-compliant hierarchy of subscriptable (indexable) classes enabling callers to validate the internal structure of arbitrarily complex scalars, data structures, and third-party objects. Like annotation objects defined by the :mod:`typing` module (e.g., :attr:`typing.Union`), these classes dynamically generate PEP-compliant type hints when subscripted (indexed) and are thus intended to annotate callables and variables. Unlike annotation objects defined by the :mod:`typing` module, these classes are *not* explicitly covered by existing PEPs and thus *not* directly usable as annotations. Instead, callers are expected to (in order): #. Annotate callable parameters and returns to be validated with :pep:`593`-compliant :attr:`typing.Annotated` type hints. #. Subscript those hints with (in order): #. The type of those parameters and returns. #. One or more subscriptions of classes declared by this submodule. ''' # ....................{ IMPORTS }.................... #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # WARNING: To avoid polluting the public module namespace, external attributes # should be locally imported at module scope *ONLY* under alternate private # names (e.g., "from argparse import ArgumentParser as _ArgumentParser" rather # than merely "from argparse import ArgumentParser"). #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! from beartype.vale._is._valeis import _IsFactory from beartype.vale._is._valeistype import ( _IsInstanceFactory, _IsSubclassFactory, ) from beartype.vale._is._valeisobj import _IsAttrFactory from beartype.vale._is._valeisoper import _IsEqualFactory # ....................{ SINGLETONS }.................... # Public factory singletons instantiating these private factory classes. Is = _IsFactory(basename='Is') IsAttr = _IsAttrFactory(basename='IsAttr') IsEqual = _IsEqualFactory(basename='IsEqual') IsInstance = _IsInstanceFactory(basename='IsInstance') IsSubclass = _IsSubclassFactory(basename='IsSubclass') # Delete all private factory classes imported above for safety. del ( _IsFactory, _IsAttrFactory, _IsEqualFactory, _IsInstanceFactory, _IsSubclassFactory, ) # ....................{ TODO }.................... #FIXME: As intelligently requested by @Saphyel at #32, add support for #additional classes support constraints resembling: # #* String constraints: # * Email. # * Uuid. # * Choice. # * Language. # * Locale. # * Country. # * Currency. #* Comparison constraints # * IdenticalTo. # * NotIdenticalTo. # * LessThan. # * GreaterThan. # * Range. # * DivisibleBy. #FIXME: Add a new BeartypeValidator.get_cause_or_none() method with the same #signature and docstring as the existing CauseSleuth.get_cause_or_none() #method. This new BeartypeValidator.get_cause_or_none() method should then be #called by the "_peperrorannotated" submodule to generate human-readable #exception messages. Note that this implies that: #* The BeartypeValidator.__init__() method will need to additionally accept a new # mandatory "get_cause_or_none: Callable[[], Optional[str]]" parameter, which # that method should then localize to "self.get_cause_or_none". #* Each __class_getitem__() dunder method of each "_BeartypeValidatorFactoryABC" subclass will need # to additionally define and pass that callable when creating and returning # its "BeartypeValidator" instance. #FIXME: *BRILLIANT IDEA.* Holyshitballstime. The idea here is that we can #leverage all of our existing "beartype.is" infrastructure to dynamically #synthesize PEP-compliant type hints that would then be implicitly supported by #any runtime type checker. At present, subscriptions of "Is" (e.g., #"Annotated[str, Is[lambda text: bool(text)]]") are only supported by beartype #itself. Of course, does anyone care? I mean, if you're using a runtime type #checker, you're probably *ONLY* using beartype. Right? That said, this would #technically improve portability by allowing users to switch between different #checkers... except not really, since they'd still have to import beartype #infrastructure to do so. So, this is probably actually useless. # #Nonetheless, the idea itself is trivial. We declare a new #"beartype.is.Portable" singleton accessed in the same way: e.g., # from beartype import beartype # from beartype.is import Portable # NonEmptyStringTest = Is[lambda text: bool(text)] # NonEmptyString = Portable[str, NonEmptyStringTest] # @beartype # def munge_it(text: NonEmptyString) -> str: ... # #So what's the difference between "typing.Annotated" and "beartype.is.Portable" #then? Simple. The latter dynamically generates one new PEP 3119-compliant #metaclass and associated class whenever subscripted. Clearly, this gets #expensive in both space and time consumption fast -- which is why this won't #be the default approach. For safety, this new class does *NOT* subclass the #first subscripted class. Instead: #* This new metaclass of this new class simply defines an __isinstancecheck__() # dunder method. For the above example, this would be: # class NonEmptyStringMetaclass(object): # def __isinstancecheck__(cls, obj) -> bool: # return isinstance(obj, str) and NonEmptyStringTest(obj) #* This new class would then be entirely empty. For the above example, this # would be: # class NonEmptyStringClass(object, metaclass=NonEmptyStringMetaclass): # pass # #Well, so much for brilliant. It's slow and big, so it seems doubtful anyone #would actually do that. Nonetheless, that's food for thought for you.

45.343511

99

0.711616

16730d6f4856a5911d4dfcf4a29a2f5449a0ddb0

3,536

py

Python

tests/test_authentication.py

movermeyer/cellardoor

25192b07224ff7bd33fd29ebac07340bef53a2ed

[ "MIT" ]

null

tests/test_authentication.py

movermeyer/cellardoor

25192b07224ff7bd33fd29ebac07340bef53a2ed

[ "MIT" ]

3

2015-01-31T14:53:06.000Z

2015-02-01T19:04:30.000Z

tests/test_authentication.py

movermeyer/cellardoor

25192b07224ff7bd33fd29ebac07340bef53a2ed

[ "MIT" ]

2

2015-01-31T14:54:28.000Z

2018-03-05T17:33:42.000Z

import unittest from mock import Mock import base64 from cellardoor import errors from cellardoor.authentication import * from cellardoor.authentication.basic import BasicAuthIdentifier

30.747826

95

0.756505

16731efe14cf79a4c56966e84b709e60bb9faf4f

42

py

Python

src/styleaug/__init__.py

somritabanerjee/speedplusbaseline

5913c611d8c182ad8070abcf5f1baffc554dfd90

[ "MIT" ]

69

2019-04-09T18:05:33.000Z

2022-03-11T05:58:59.000Z

src/styleaug/__init__.py

somritabanerjee/speedplusbaseline

5913c611d8c182ad8070abcf5f1baffc554dfd90

[ "MIT" ]

6

2019-04-01T12:04:10.000Z

2022-01-19T11:49:13.000Z

src/styleaug/__init__.py

somritabanerjee/speedplusbaseline

5913c611d8c182ad8070abcf5f1baffc554dfd90

[ "MIT" ]

13

2019-05-22T19:08:36.000Z

2021-08-13T01:21:47.000Z

from .styleAugmentor import StyleAugmentor

42

0.904762

167422ad1c22d904c1fb3127c28d48e06243100c

2,698

py

Python

configs/classification/imagenet/mixups/convnext/convnext_tiny_smooth_mix_8xb256_accu2_ema_fp16.py

Westlake-AI/openmixup

ea81250819e740dd823e30cb7ce382d14a3c1b91

[ "Apache-2.0" ]

10

2021-12-30T10:22:27.000Z

2022-03-30T02:31:38.000Z

configs/classification/imagenet/mixups/convnext/convnext_tiny_smooth_mix_8xb256_accu2_ema_fp16.py

Westlake-AI/openmixup

ea81250819e740dd823e30cb7ce382d14a3c1b91

[ "Apache-2.0" ]

3

2022-01-20T21:02:48.000Z

2022-03-19T13:49:45.000Z

configs/classification/imagenet/mixups/convnext/convnext_tiny_smooth_mix_8xb256_accu2_ema_fp16.py

Westlake-AI/openmixup

ea81250819e740dd823e30cb7ce382d14a3c1b91

[ "Apache-2.0" ]

null

_base_ = [ '../../../_base_/datasets/imagenet/swin_sz224_4xbs256.py', '../../../_base_/default_runtime.py', ] # model settings model = dict( type='MixUpClassification', pretrained=None, alpha=0.2, mix_mode="cutmix", mix_args=dict( attentivemix=dict(grid_size=32, top_k=None, beta=8), # AttentiveMix+ in this repo (use pre-trained) automix=dict(mask_adjust=0, lam_margin=0), # require pre-trained mixblock fmix=dict(decay_power=3, size=(224,224), max_soft=0., reformulate=False), manifoldmix=dict(layer=(0, 3)), puzzlemix=dict(transport=True, t_batch_size=32, t_size=-1, # adjust t_batch_size if CUDA out of memory mp=None, block_num=4, # block_num<=4 and mp=2/4 for fast training beta=1.2, gamma=0.5, eta=0.2, neigh_size=4, n_labels=3, t_eps=0.8), resizemix=dict(scope=(0.1, 0.8), use_alpha=True), samix=dict(mask_adjust=0, lam_margin=0.08), # require pre-trained mixblock ), backbone=dict( type='ConvNeXt', arch='tiny', out_indices=(3,), norm_cfg=dict(type='LN2d', eps=1e-6), act_cfg=dict(type='GELU'), drop_path_rate=0.1, gap_before_final_norm=True, ), head=dict( type='ClsMixupHead', # mixup CE + label smooth loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), with_avg_pool=False, # gap_before_final_norm is True in_channels=768, num_classes=1000) ) # interval for accumulate gradient update_interval = 2 # total: 8 x bs256 x 2 accumulates = bs4096 # additional hooks custom_hooks = [ dict(type='EMAHook', # EMA_W = (1 - m) * EMA_W + m * W momentum=0.9999, warmup='linear', warmup_iters=20 * 626, warmup_ratio=0.9, # warmup 20 epochs. update_interval=update_interval, ), ] # optimizer optimizer = dict( type='AdamW', lr=4e-3, # lr = 5e-4 * (256 * 4) * 4 accumulate / 1024 = 4e-3 / bs4096 weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), paramwise_options={ '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), 'bias': dict(weight_decay=0.), }) # apex use_fp16 = True fp16 = dict(type='apex', loss_scale=dict(init_scale=512., mode='dynamic')) optimizer_config = dict(grad_clip=None, update_interval=update_interval, use_fp16=use_fp16) # lr scheduler lr_config = dict( policy='CosineAnnealing', by_epoch=False, min_lr=1e-5, warmup='linear', warmup_iters=20, warmup_by_epoch=True, # warmup 20 epochs. warmup_ratio=1e-6, ) # runtime settings runner = dict(type='EpochBasedRunner', max_epochs=300)

34.151899

111

0.640474

16748f009db0117be1d076ddc5a413db7e45e64c

2,274

py

Python

mcstasscript/interface/reader.py

PaNOSC-ViNYL/McStasScript

bd94ebc6cac290c3c9662871df40d76edbe4a44e

[ "BSD-3-Clause" ]

3

2019-08-29T14:15:06.000Z

2021-03-04T12:08:48.000Z

mcstasscript/interface/reader.py

PaNOSC-ViNYL/McStasScript

bd94ebc6cac290c3c9662871df40d76edbe4a44e

[ "BSD-3-Clause" ]

37

2019-03-05T12:28:32.000Z

2022-03-22T10:11:23.000Z

mcstasscript/interface/reader.py

PaNOSC-ViNYL/McStasScript

bd94ebc6cac290c3c9662871df40d76edbe4a44e

[ "BSD-3-Clause" ]

6

2019-10-21T20:19:10.000Z

2022-03-09T10:12:16.000Z

import os from mcstasscript.instr_reader.control import InstrumentReader from mcstasscript.interface.instr import McStas_instr

28.425

79

0.579595

1676599bdfdd4b081bb8bb20aa32589f69c604ef

3,701

py

Python

src/regrtest.py

ucsd-progsys/csolve-bak

89cfeb5403e617f45ece4bae9f88f8e6cd7ca934

[ "BSD-3-Clause" ]

null

src/regrtest.py

ucsd-progsys/csolve-bak

89cfeb5403e617f45ece4bae9f88f8e6cd7ca934

[ "BSD-3-Clause" ]

1

2018-04-24T10:43:07.000Z

src/regrtest.py

ucsd-progsys/csolve-bak

89cfeb5403e617f45ece4bae9f88f8e6cd7ca934

[ "BSD-3-Clause" ]

null

#!/usr/bin/python # Copyright (c) 2009 The Regents of the University of California. All rights reserved. # # Permission is hereby granted, without written agreement and without # license or royalty fees, to use, copy, modify, and distribute this # software and its documentation for any purpose, provided that the # above copyright notice and the following two paragraphs appear in # all copies of this software. # # IN NO EVENT SHALL THE UNIVERSITY OF CALIFORNIA BE LIABLE TO ANY PARTY # FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES # ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN # IF THE UNIVERSITY OF CALIFORNIA HAS BEEN ADVISED OF THE POSSIBILITY # OF SUCH DAMAGE. # # THE UNIVERSITY OF CALIFORNIA SPECIFICALLY DISCLAIMS ANY WARRANTIES, # INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY # AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS # ON AN "AS IS" BASIS, AND THE UNIVERSITY OF CALIFORNIA HAS NO OBLIGATION # TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. import time, subprocess, optparse, sys, socket, os import misc.rtest as rtest solve = "./csolve -c".split() null = open("/dev/null", "w") now = (time.asctime(time.localtime(time.time()))).replace(" ","_") logfile = "../tests/logs/regrtest_results_%s_%s" % (socket.gethostname (), now) argcomment = "//! run with " ##################################################################################### #testdirs = [("../postests", 0)] #testdirs = [("../negtests", 1)] #testdirs = [("../slowtests", 1)] #DEFAULT testdirs = [("../tests/postests", 0), ("../tests/negtests", [1, 2])] #testdirs = [("../tests/microtests", 0)] parser = optparse.OptionParser() parser.add_option("-t", "--threads", dest="threadcount", default=1, type=int, help="spawn n threads") parser.add_option("-o", "--opts", dest="opts", default="", type=str, help="additional arguments to csolve") parser.disable_interspersed_args() options, args = parser.parse_args() runner = rtest.TestRunner (Config (options.opts, testdirs, logfile, options.threadcount)) exit (runner.run ())

38.154639

107

0.676574

16766ccc57f251df7ba9394a55b7eabdd7d12e46

2,925

py

Python

country_capital_guesser.py

NathanMH/ComputerClub

197585c1a77f71ee363547740d6e09f945e7526f

[ "MIT" ]

null

country_capital_guesser.py

NathanMH/ComputerClub

197585c1a77f71ee363547740d6e09f945e7526f

[ "MIT" ]

null

country_capital_guesser.py

NathanMH/ComputerClub

197585c1a77f71ee363547740d6e09f945e7526f

[ "MIT" ]

null

#! /usr/bin/env python3 ####################### """#################### Index: 1. Imports and Readme 2. Functions 3. Main 4. Testing ####################""" ####################### ################################################################### # 1. IMPORTS AND README ################################################################### import easygui import country_list_getter ################################################################### # 2. FUNCTIONS ################################################################### # Dictionary. It has keys (Canada, France etc...) and Values (Paris, Ottawa) country_list_getter.main() COUNTRIES_CAPITALS = country_list_getter.FINAL_LIST ################################################################### # 3. MAIN ################################################################### ################################################################### # 4. TESTING ################################################################### # COUNTRIES_CAPITALS = {"Canada": "Ottawa", "United States": "Washington", "France": "Paris"} # ask_to_play() # main_question_box("Canada") funtime()

33.62069

160

0.494017

1676c1cee546273be3e4746fcf8ddcdf0ca583bb

2,288

py

Python

data_analysis/audiocommons_ffont/scripts/rekordbox_xml_to_analysis_rhythm_rekordbox_file.py

aframires/freesound-loop-annotator

a24e0c23bfc671e41e8627150e7b9fcae5c8cb13

[ "Apache-2.0" ]

18

2020-01-22T14:58:18.000Z

2022-02-21T12:07:51.000Z

data_analysis/audiocommons_ffont/scripts/rekordbox_xml_to_analysis_rhythm_rekordbox_file.py

aframires/freesound-loop-annotator

a24e0c23bfc671e41e8627150e7b9fcae5c8cb13

[ "Apache-2.0" ]

2

2020-02-24T13:14:05.000Z

2020-09-21T13:34:53.000Z

data_analysis/audiocommons_ffont/scripts/rekordbox_xml_to_analysis_rhythm_rekordbox_file.py

aframires/freesound-loop-annotator

a24e0c23bfc671e41e8627150e7b9fcae5c8cb13

[ "Apache-2.0" ]

1

2020-01-22T14:55:36.000Z

# Need this to import from parent directory when running outside pycharm import os import sys sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir)) from ac_utils.general import save_to_json, load_from_json import click import xml.etree.ElementTree from urllib import unquote if __name__ == '__main__': rekordbox_file_to_analysis_file()

39.448276

119

0.660402

1676d72870f651008f4e3aca9c90ccf681a85a4a

5,947

py

Python

inventree/part.py

SergeoLacruz/inventree-python

94681428f61de4ca51171e685812ebc436b9be42

[ "MIT" ]

null

inventree/part.py

SergeoLacruz/inventree-python

94681428f61de4ca51171e685812ebc436b9be42

[ "MIT" ]

null

inventree/part.py

SergeoLacruz/inventree-python

94681428f61de4ca51171e685812ebc436b9be42

[ "MIT" ]

null

# -*- coding: utf-8 -*- import logging import re import inventree.base import inventree.stock import inventree.company import inventree.build logger = logging.getLogger('inventree')

27.920188

82

0.626534

167719b0cc59eef9b7fff6f4ce109cd0d2fe8bc1

12,932

py

Python

tests/test_web_urldispatcher.py

avstarkov/aiohttp

b0a03cffccf677bf316227522a9b841c15dcb869

[ "Apache-2.0" ]

null

tests/test_web_urldispatcher.py

avstarkov/aiohttp

b0a03cffccf677bf316227522a9b841c15dcb869

[ "Apache-2.0" ]

null

tests/test_web_urldispatcher.py

avstarkov/aiohttp

b0a03cffccf677bf316227522a9b841c15dcb869

[ "Apache-2.0" ]

null

import functools import os import shutil import tempfile from unittest import mock from unittest.mock import MagicMock import pytest from aiohttp import abc, web from aiohttp.web_urldispatcher import SystemRoute async def test_follow_symlink(tmp_dir_path, aiohttp_client): """ Tests the access to a symlink, in static folder """ data = 'hello world' my_dir_path = os.path.join(tmp_dir_path, 'my_dir') os.mkdir(my_dir_path) my_file_path = os.path.join(my_dir_path, 'my_file_in_dir') with open(my_file_path, 'w') as fw: fw.write(data) my_symlink_path = os.path.join(tmp_dir_path, 'my_symlink') os.symlink(my_dir_path, my_symlink_path) app = web.Application() # Register global static route: app.router.add_static('/', tmp_dir_path, follow_symlinks=True) client = await aiohttp_client(app) # Request the root of the static directory. r = await client.get('/my_symlink/my_file_in_dir') assert r.status == 200 assert (await r.text()) == data async def test_access_non_existing_resource(tmp_dir_path, aiohttp_client): """ Tests accessing non-existing resource Try to access a non-exiting resource and make sure that 404 HTTP status returned. """ app = web.Application() # Register global static route: app.router.add_static('/', tmp_dir_path, show_index=True) client = await aiohttp_client(app) # Request the root of the static directory. r = await client.get('/non_existing_resource') assert r.status == 404 async def test_handler_metadata_persistence(): """ Tests accessing metadata of a handler after registering it on the app router. """ app = web.Application() def sync_handler(request): """Doc""" return web.Response() app.router.add_get('/async', async_handler) with pytest.warns(DeprecationWarning): app.router.add_get('/sync', sync_handler) for resource in app.router.resources(): for route in resource: assert route.handler.__doc__ == 'Doc' def test_system_route(): route = SystemRoute(web.HTTPCreated(reason='test')) with pytest.raises(RuntimeError): route.url_for() assert route.name is None assert route.resource is None assert "<SystemRoute 201: test>" == repr(route) assert 201 == route.status assert 'test' == route.reason def test_resource_raw_match(): app = web.Application() route = app.router.add_get("/a", handler, name="a") assert route.resource.raw_match("/a") route = app.router.add_get("/{b}", handler, name="b") assert route.resource.raw_match("/{b}") resource = app.router.add_static("/static", ".") assert not resource.raw_match("/static")

27.514894

79

0.634009

1678ba6ffacdb3dc2a1730ee864aab5b2813d801

13,683

py

Python

R-GMM-VGAE/model_citeseer.py

nairouz/R-GAE

acc7bfe36153a4c7d6f68e21a557bb4d99dab639

[ "MIT" ]

26

2021-07-18T01:31:48.000Z

2022-03-31T03:23:11.000Z

R-GMM-VGAE/model_citeseer.py

Fawzidev/R-GAE

80988ddf951f1723091a04b617ce4fc6d20ab9ce

[ "MIT" ]

3

2021-10-01T07:24:42.000Z

2021-11-03T14:25:55.000Z

R-GMM-VGAE/model_citeseer.py

Fawzidev/R-GAE

80988ddf951f1723091a04b617ce4fc6d20ab9ce

[ "MIT" ]

7

2021-07-18T01:47:01.000Z

2022-01-24T21:09:10.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Authors : Nairouz Mrabah ([email protected]) & Mohamed Fawzi Touati ([email protected]) # @Paper : Rethinking Graph Autoencoder Models for Attributed Graph Clustering # @License : MIT License import torch import numpy as np import torch.nn as nn import scipy.sparse as sp import torch.nn.functional as F from tqdm import tqdm from torch.optim import Adam from sklearn.mixture import GaussianMixture from torch.optim.lr_scheduler import StepLR from preprocessing import sparse_to_tuple from sklearn.neighbors import NearestNeighbors from sklearn import metrics from munkres import Munkres

46.699659

259

0.625448

16796b947c516147ed6529d69a08e17bbd4afe73

3,005

py

Python

odoo-13.0/addons/stock_account/models/account_chart_template.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

odoo-13.0/addons/stock_account/models/account_chart_template.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

odoo-13.0/addons/stock_account/models/account_chart_template.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Part of Odoo. See LICENSE file for full copyright and licensing details. from odoo import api, models, _ import logging _logger = logging.getLogger(__name__)

47.698413

180

0.577704

167a0dd80799c1a419238ba6164d01472b85e5d4

6,094

py

Python

lib/roi_data/loader.py

BarneyQiao/pcl.pytorch

4e0280e5e1470f705e620eda26f881d627c5016c

[ "MIT" ]

233

2019-05-10T07:17:42.000Z

2022-03-30T09:24:16.000Z

lib/roi_data/loader.py

Michael-Steven/Crack_Image_WSOD

4e8591a7c0768cee9eb7240bb9debd54824f5b33

[ "MIT" ]

78

2019-05-10T21:10:47.000Z

2022-03-29T13:57:32.000Z

lib/roi_data/loader.py

Michael-Steven/Crack_Image_WSOD

4e8591a7c0768cee9eb7240bb9debd54824f5b33

[ "MIT" ]

57

2019-05-10T07:17:37.000Z

2022-03-24T04:43:24.000Z

import math import numpy as np import numpy.random as npr import torch import torch.utils.data as data import torch.utils.data.sampler as torch_sampler from torch.utils.data.dataloader import default_collate from torch._six import int_classes as _int_classes from core.config import cfg from roi_data.minibatch import get_minibatch import utils.blob as blob_utils # from model.rpn.bbox_transform import bbox_transform_inv, clip_boxes def cal_minibatch_ratio(ratio_list): """Given the ratio_list, we want to make the RATIO same for each minibatch on each GPU. Note: this only work for 1) cfg.TRAIN.MAX_SIZE is ignored during `prep_im_for_blob` and 2) cfg.TRAIN.SCALES containing SINGLE scale. Since all prepared images will have same min side length of cfg.TRAIN.SCALES[0], we can pad and batch images base on that. """ DATA_SIZE = len(ratio_list) ratio_list_minibatch = np.empty((DATA_SIZE,)) num_minibatch = int(np.ceil(DATA_SIZE / cfg.TRAIN.IMS_PER_BATCH)) # Include leftovers for i in range(num_minibatch): left_idx = i * cfg.TRAIN.IMS_PER_BATCH right_idx = min((i+1) * cfg.TRAIN.IMS_PER_BATCH - 1, DATA_SIZE - 1) if ratio_list[right_idx] < 1: # for ratio < 1, we preserve the leftmost in each batch. target_ratio = ratio_list[left_idx] elif ratio_list[left_idx] > 1: # for ratio > 1, we preserve the rightmost in each batch. target_ratio = ratio_list[right_idx] else: # for ratio cross 1, we make it to be 1. target_ratio = 1 ratio_list_minibatch[left_idx:(right_idx+1)] = target_ratio return ratio_list_minibatch def collate_minibatch(list_of_blobs): """Stack samples seperately and return a list of minibatches A batch contains NUM_GPUS minibatches and image size in different minibatch may be different. Hence, we need to stack smaples from each minibatch seperately. """ Batch = {key: [] for key in list_of_blobs[0]} # Because roidb consists of entries of variable length, it can't be batch into a tensor. # So we keep roidb in the type of "list of ndarray". lists = [] for blobs in list_of_blobs: lists.append({'data' : blobs.pop('data'), 'rois' : blobs.pop('rois'), 'labels' : blobs.pop('labels')}) for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH): mini_list = lists[i:(i + cfg.TRAIN.IMS_PER_BATCH)] minibatch = default_collate(mini_list) for key in minibatch: Batch[key].append(minibatch[key]) return Batch

38.56962

97

0.639317

167a8c5cf5187907cc0dbc578ad93057948ece69

28,272

py

Python

venv/Lib/site-packages/sklearn/linear_model/tests/test_least_angle.py

andywu113/fuhe_predict

7fd816ae83467aa659d420545cd3e25a5e933d5f

[ "MIT" ]

3

2019-06-05T12:11:20.000Z

2022-01-17T13:53:06.000Z

venv/Lib/site-packages/sklearn/linear_model/tests/test_least_angle.py

kevinten10/Clothing-Classification

9aac6e339651137179f4e4da36fe7743cf4bdca4

[ "MIT" ]

3

2021-06-08T20:58:27.000Z

2022-03-12T00:16:49.000Z

venv/Lib/site-packages/sklearn/linear_model/tests/test_least_angle.py

kevinten10/Clothing-Classification

9aac6e339651137179f4e4da36fe7743cf4bdca4

[ "MIT" ]

1

2019-02-11T22:36:12.000Z

import warnings from distutils.version import LooseVersion import numpy as np import pytest from scipy import linalg from sklearn.model_selection import train_test_split from sklearn.utils.testing import assert_allclose from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_less from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_raises from sklearn.utils.testing import ignore_warnings from sklearn.utils.testing import assert_warns from sklearn.utils.testing import TempMemmap from sklearn.exceptions import ConvergenceWarning from sklearn import linear_model, datasets from sklearn.linear_model.least_angle import _lars_path_residues, LassoLarsIC # TODO: use another dataset that has multiple drops diabetes = datasets.load_diabetes() X, y = diabetes.data, diabetes.target G = np.dot(X.T, X) Xy = np.dot(X.T, y) n_samples = y.size def test_collinearity(): # Check that lars_path is robust to collinearity in input X = np.array([[3., 3., 1.], [2., 2., 0.], [1., 1., 0]]) y = np.array([1., 0., 0]) rng = np.random.RandomState(0) f = ignore_warnings _, _, coef_path_ = f(linear_model.lars_path)(X, y, alpha_min=0.01) assert not np.isnan(coef_path_).any() residual = np.dot(X, coef_path_[:, -1]) - y assert_less((residual ** 2).sum(), 1.) # just make sure it's bounded n_samples = 10 X = rng.rand(n_samples, 5) y = np.zeros(n_samples) _, _, coef_path_ = linear_model.lars_path(X, y, Gram='auto', copy_X=False, copy_Gram=False, alpha_min=0., method='lasso', verbose=0, max_iter=500) assert_array_almost_equal(coef_path_, np.zeros_like(coef_path_)) def test_no_path(): # Test that the ``return_path=False`` option returns the correct output alphas_, _, coef_path_ = linear_model.lars_path( X, y, method='lar') alpha_, _, coef = linear_model.lars_path( X, y, method='lar', return_path=False) assert_array_almost_equal(coef, coef_path_[:, -1]) assert alpha_ == alphas_[-1] def test_no_path_precomputed(): # Test that the ``return_path=False`` option with Gram remains correct alphas_, _, coef_path_ = linear_model.lars_path( X, y, method='lar', Gram=G) alpha_, _, coef = linear_model.lars_path( X, y, method='lar', Gram=G, return_path=False) assert_array_almost_equal(coef, coef_path_[:, -1]) assert alpha_ == alphas_[-1] def test_no_path_all_precomputed(): # Test that the ``return_path=False`` option with Gram and Xy remains # correct X, y = 3 * diabetes.data, diabetes.target G = np.dot(X.T, X) Xy = np.dot(X.T, y) alphas_, _, coef_path_ = linear_model.lars_path( X, y, method='lasso', Xy=Xy, Gram=G, alpha_min=0.9) alpha_, _, coef = linear_model.lars_path( X, y, method='lasso', Gram=G, Xy=Xy, alpha_min=0.9, return_path=False) assert_array_almost_equal(coef, coef_path_[:, -1]) assert alpha_ == alphas_[-1] def test_lasso_lars_ic(): # Test the LassoLarsIC object by checking that # - some good features are selected. # - alpha_bic > alpha_aic # - n_nonzero_bic < n_nonzero_aic lars_bic = linear_model.LassoLarsIC('bic') lars_aic = linear_model.LassoLarsIC('aic') rng = np.random.RandomState(42) X = diabetes.data X = np.c_[X, rng.randn(X.shape[0], 5)] # add 5 bad features lars_bic.fit(X, y) lars_aic.fit(X, y) nonzero_bic = np.where(lars_bic.coef_)[0] nonzero_aic = np.where(lars_aic.coef_)[0] assert_greater(lars_bic.alpha_, lars_aic.alpha_) assert_less(len(nonzero_bic), len(nonzero_aic)) assert_less(np.max(nonzero_bic), diabetes.data.shape[1]) # test error on unknown IC lars_broken = linear_model.LassoLarsIC('<unknown>') assert_raises(ValueError, lars_broken.fit, X, y) def test_lars_path_readonly_data(): # When using automated memory mapping on large input, the # fold data is in read-only mode # This is a non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/4597 splitted_data = train_test_split(X, y, random_state=42) with TempMemmap(splitted_data) as (X_train, X_test, y_train, y_test): # The following should not fail despite copy=False _lars_path_residues(X_train, y_train, X_test, y_test, copy=False) # now we gonna test the positive option for all estimator classes default_parameter = {'fit_intercept': False} estimator_parameter_map = {'LassoLars': {'alpha': 0.1}, 'LassoLarsCV': {}, 'LassoLarsIC': {}}

38.360923

79

0.616051

167b4e3bb5a00625d3f0b289e41e2bc170fabc61

3,128

py

Python

parser.py

FeroxTL/pynginxconfig-new

71cb78c635930b0a764d3274646d436e8d2f1c4d

[ "MIT" ]

8

2016-03-25T04:22:39.000Z

2022-02-12T21:46:47.000Z

parser.py

Winnerer/pynginxconfig

71cb78c635930b0a764d3274646d436e8d2f1c4d

[ "MIT" ]

null

parser.py

Winnerer/pynginxconfig

71cb78c635930b0a764d3274646d436e8d2f1c4d

[ "MIT" ]

3

2019-01-26T15:54:54.000Z

2022-02-12T21:46:47.000Z

#coding: utf8 import copy import re from blocks import Block, EmptyBlock, KeyValueOption, Comment, Location qwe = EmptyBlock() parse("""#{ asd #qweqeqwe{} servername qweqweqweqweqwe; # comment {lalalal} #1 server { listen 8080 tls; root /data/up1; location / { l200; } location /qwe{ s 500; }#123 }#qweqwe""", qwe) print(qwe.render()) qwe = EmptyBlock() parse(""" servername wqeqweqwe; http { ## # Basic Settings ## sendfile on; tcp_nopush on; tcp_nodelay on; keepalive_timeout 65; types_hash_max_size 2048; # server_tokens off; # server_names_hash_bucket_size 64; # server_name_in_redirect off; include /etc/nginx/mime.types; default_type application/octet-stream; ## # Logging Settings ## access_log /var/log/nginx/access.log; error_log /var/log/nginx/error.log; ## # Gzip Settings ## gzip on; gzip_disable "msie6"; }#123123 """, qwe) print(qwe.render())

24.825397

113

0.545716

167b69684843eed85973a69dafe6205fbdff9406

845

py

Python

cocos2d/tools/jenkins-scripts/configs/cocos-2dx-develop-win32.py

triompha/EarthWarrior3D

d68a347902fa1ca1282df198860f5fb95f326797

[ "MIT" ]

null

cocos2d/tools/jenkins-scripts/configs/cocos-2dx-develop-win32.py

triompha/EarthWarrior3D

d68a347902fa1ca1282df198860f5fb95f326797

[ "MIT" ]

null

cocos2d/tools/jenkins-scripts/configs/cocos-2dx-develop-win32.py

triompha/EarthWarrior3D

d68a347902fa1ca1282df198860f5fb95f326797

[ "MIT" ]

null

import os import subprocess import sys print 'Build Config:' print ' Host:win7 x86' print ' Branch:develop' print ' Target:win32' print ' "%VS110COMNTOOLS%..\IDE\devenv.com" "build\cocos2d-win32.vc2012.sln" /Build "Debug|Win32"' if(os.path.exists('build/cocos2d-win32.vc2012.sln') == False): node_name = os.environ['NODE_NAME'] source_dir = '../cocos-2dx-develop-base-repo/node/' + node_name source_dir = source_dir.replace("/", os.sep) os.system("xcopy " + source_dir + " . /E /Y /H") os.system('git pull origin develop') os.system('git submodule update --init --force') ret = subprocess.call('"%VS110COMNTOOLS%..\IDE\devenv.com" "build\cocos2d-win32.vc2012.sln" /Build "Debug|Win32"', shell=True) os.system('git clean -xdf -f') print 'build exit' print ret if ret == 0: exit(0) else: exit(1)

33.8

127

0.668639

167cfaccf65c4a217ee921178f5ab5094fc6d8a6

241

py

Python

iris_sdk/models/data/ord/rate_center_search_order.py

NumberAI/python-bandwidth-iris

0e05f79d68b244812afb97e00fd65b3f46d00aa3

[ "MIT" ]

2

2020-04-13T13:47:59.000Z

2022-02-23T20:32:41.000Z

iris_sdk/models/data/ord/rate_center_search_order.py

bandwidthcom/python-bandwidth-iris

dbcb30569631395041b92917252d913166f7d3c9

[ "MIT" ]

5

2020-09-18T20:59:24.000Z

2021-08-25T16:51:42.000Z

iris_sdk/models/data/ord/rate_center_search_order.py

bandwidthcom/python-bandwidth-iris

dbcb30569631395041b92917252d913166f7d3c9

[ "MIT" ]

5

2018-12-12T14:39:50.000Z

2020-11-17T21:42:29.000Z

#!/usr/bin/env python from iris_sdk.models.base_resource import BaseData from iris_sdk.models.maps.ord.rate_center_search_order import \ RateCenterSearchOrderMap

30.125

64

0.834025

167df72d7c85276ff20ea4552c3c38a522dba306

7,024

py

Python

optimizer.py

thanusha22/CEC-1

02ad9247b006a348cc871a5714cf5abfa4a516af

[ "MIT" ]

null

optimizer.py

thanusha22/CEC-1

02ad9247b006a348cc871a5714cf5abfa4a516af

[ "MIT" ]

null

optimizer.py

thanusha22/CEC-1

02ad9247b006a348cc871a5714cf5abfa4a516af

[ "MIT" ]

null

from pathlib import Path import optimizers.PSO as pso import optimizers.MVO as mvo import optimizers.GWO as gwo import optimizers.MFO as mfo import optimizers.CS as cs import optimizers.BAT as bat import optimizers.WOA as woa import optimizers.FFA as ffa import optimizers.SSA as ssa import optimizers.GA as ga import optimizers.HHO as hho import optimizers.SCA as sca import optimizers.JAYA as jaya import optimizers.HYBRID as hybrid import benchmarks import csv import numpy import time import warnings import os import plot_convergence as conv_plot import plot_boxplot as box_plot warnings.simplefilter(action="ignore") def run(optimizer, objectivefunc, NumOfRuns, params, export_flags): """ It serves as the main interface of the framework for running the experiments. Parameters ---------- optimizer : list The list of optimizers names objectivefunc : list The list of benchmark functions NumOfRuns : int The number of independent runs params : set The set of parameters which are: 1. Size of population (PopulationSize) 2. The number of iterations (Iterations) export_flags : set The set of Boolean flags which are: 1. Export (Exporting the results in a file) 2. Export_details (Exporting the detailed results in files) 3. Export_convergence (Exporting the covergence plots) 4. Export_boxplot (Exporting the box plots) Returns ----------- N/A """ # Select general parameters for all optimizers (population size, number of iterations) .... PopulationSize = params["PopulationSize"] Iterations = params["Iterations"] # Export results ? Export = export_flags["Export_avg"] Export_details = export_flags["Export_details"] Export_convergence = export_flags["Export_convergence"] Export_boxplot = export_flags["Export_boxplot"] Flag = False Flag_details = False # CSV Header for for the cinvergence CnvgHeader = [] results_directory = time.strftime("%Y-%m-%d-%H-%M-%S") + "/" Path(results_directory).mkdir(parents=True, exist_ok=True) for l in range(0, Iterations): CnvgHeader.append("Iter" + str(l + 1)) for i in range(0, len(optimizer)): for j in range(0, len(objectivefunc)): convergence = [0] * NumOfRuns executionTime = [0] * NumOfRuns for k in range(0, NumOfRuns): func_details = benchmarks.getFunctionDetails(objectivefunc[j]) x = selector(optimizer[i], func_details, PopulationSize, Iterations) convergence[k] = x.convergence optimizerName = x.optimizer objfname = x.objfname if Export_details == True: ExportToFile = results_directory + "experiment_details.csv" with open(ExportToFile, "a", newline="\n") as out: writer = csv.writer(out, delimiter=",") if ( Flag_details == False ): # just one time to write the header of the CSV file header = numpy.concatenate( [["Optimizer", "objfname", "ExecutionTime"], CnvgHeader] ) writer.writerow(header) Flag_details = True # at least one experiment executionTime[k] = x.executionTime a = numpy.concatenate( [[x.optimizer, x.objfname, x.executionTime], x.convergence] ) writer.writerow(a) out.close() if Export == True: ExportToFile = results_directory + "experiment.csv" with open(ExportToFile, "a", newline="\n") as out: writer = csv.writer(out, delimiter=",") if ( Flag == False ): # just one time to write the header of the CSV file header = numpy.concatenate( [["Optimizer", "objfname", "ExecutionTime"], CnvgHeader] ) writer.writerow(header) Flag = True avgExecutionTime = float("%0.2f" % (sum(executionTime) / NumOfRuns)) avgConvergence = numpy.around( numpy.mean(convergence, axis=0, dtype=numpy.float64), decimals=2 ).tolist() a = numpy.concatenate( [[optimizerName, objfname, avgExecutionTime], avgConvergence] ) writer.writerow(a) out.close() if Export_convergence == True: conv_plot.run(results_directory, optimizer, objectivefunc, Iterations) if Export_boxplot == True: box_plot.run(results_directory, optimizer, objectivefunc, Iterations) if Flag == False: # Faild to run at least one experiment print( "No Optomizer or Cost function is selected. Check lists of available optimizers and cost functions" ) print("Execution completed")

38.173913

111

0.58955

167e133f17b315eee99f736bb553b46a271cd9cc

1,614

py

Python

tests/fields/test_primitive_types.py

slawak/dataclasses-avroschema

04e69a176b3e72bfa0acd3edbd044ecd161b1a68

[ "MIT" ]

null

tests/fields/test_primitive_types.py

slawak/dataclasses-avroschema

04e69a176b3e72bfa0acd3edbd044ecd161b1a68

[ "MIT" ]

null

tests/fields/test_primitive_types.py

slawak/dataclasses-avroschema

04e69a176b3e72bfa0acd3edbd044ecd161b1a68

[ "MIT" ]

null

import dataclasses import pytest from dataclasses_avroschema import fields from . import consts

34.340426

87

0.76456